Hybrid Content Dynamic Recommendation System Based in Adapted Tags and Applied to Digital Library

In figure 1 there are two databases, one referring to the library management system and another representing the institutional repository. Institutional repository is stored digital documents such as theses, dissertations, and articles. The library database stores all information necessary for management of academic activities, and settled daily through actions of users (students, faculty, and staff). When a user loan a book, a lot of information about this action are stored in the database. This information contains metadata that will be used to extract tags and map the user profile. Metadata is formed by the following attributes:

Metadata of the users loans history:

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  User name: user name who made the book loan

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  Student ID: unique numerical value that identifies a student in the system

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  Cod Department: the department code to which the student who made the loan is linked

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  Keyword (tags): This metadata is related to information in the book that was borrowed by the user. This information is manipulated and processed into tags.

Books metadata:

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  Book Title: corresponds to the name of the book

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  Keywords (tags): This field stores information as book author, area and keywords that characterize the content of the book.

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  Cod Collection: unique numerical value that identifies a particular book

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  Classification: code that classifies a book according to the area of concentration

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  Date of loan: date referring to the book of the loan made by the user

This information is used to generate the list of tags that form each user’s profile. To better get user preferences with more accurate information, a list of books is sent as a recommendation, which is evaluated with a note for each item that was recommended. Thus, the rating impact on the user list tags and improve the quality of recommendations.

The information from the repository database documents is integrated with the library database to generate recommendations from both databases. In addition, to being an alternative to complement the variety of information, it also contributes to promoting the use of the repository, since users will have a single interface to access content from both systems. The attributes used to extract information from the digital document metadata are:

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  Document title: title of the document stored in the repository

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  Keywords: terms that are used as key words of the document

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  Abstract: textual description used to describe the subject covered in the document

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  Author: identifies the people who developed the work

At this stage, the data were collected from the database will be filtered to keep only what is relevant to the search. So, we apply techniques of selection and processing of data used in most systems that work with data mining or retrieval information.

First, the stopwords are removed because even if used to form a sentence, rarely contribute to add meaning to the words. The stopwords are considered words that occur with a high frequency in the documents, but potentially become useless because they are very common, not contributing to the relevance of the research. Examples of such words are: the, and, to where, that, was, this, among others. A stopwords file is formed to be used by the system to remove from the information retrieved, all existing words in this list [Elmasri and Navathe and De Oliveira Morais, 2011].

Other removals are made to maintain the quality of the information retrieved. Words with less than two characters are disregarded, and to facilitate the analysis are removed accents, special characters, and all the words are converted to lowercase.

In the first test is applied stemming process, also known as stemming technique. This process is used in many computer applications to transform the variant forms of a word in a more accurate representation, and that is generic enough to capture the essence of the words [Alvares, 2014]. The approach used in stemming process applied in this study was based on Snowball algorithm, a variation of Porter’s algorithm, which uses the removal of suffixes [Porter, 1999] [Xavier and Gomes and Silva, 2013]. However, application of this technique was not satisfactory for this experiment. When using stemming algorithm accuracy decreases. A possible cause may be the creation of keywords (tags). In this context the words are not created freely by the user, but by specialists who analyze the terms before registering the information in document metadata. Therefore, considering the loss of accuracy and also the demand for computational processing to perform this task, the stemming utilization in this application was not continued.

Some studies adopt a model to represent features in documents, known in Information Retrieval as Bag of words [Jiang etal., 2010] [Luo and Wei and Lai, 2011] [Lin etal., 2016], and has been considered promising in the content classification. In this model, any textual content is transformed into a set of words that convey some meaning and are used as the basis for classification of a given resource. Following this concept, we adapt the model for our study to use the tags list (bag of tags).

The tags lists are formed by a set of words extracted from books metadata that are collected in two ways: record of the books that were borrowed by users in the databases and lists (feedback) of recommended books for the user. Each term or word is considered a tag that expresses a meaning that classifies the content to which this tag was extracted. In this case, if a user is borrowing a book whose title is “Computer Programming”, the extracted tags of this book metadata represent its context, and therefore it indicates that the user is interested in documents relating to this subject. Each user profile is traced to recommend content, based on their tags set.

In this work, we implemented a method that consists of five lists of tags that are applied during the recommendation process. This phase is illustrated in figure 1, but to facilitate the understanding it is represented in more details in figure 2.

The five lists of tags shown in figure 2 are: list of document tags, list of books tags, list of user tags, list of area tags, and lists of irrelevant tags. With the exception of the list of document tags and books, the other three can be considered lists of dynamic tags because they are changed over time according to the changes of user’s interest.

At this stage the tag lists are compared to generate recommendations. We consider three threshold groups defined based on the weight of the tags:

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  Group 1: $\geq 70\%$;

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  Group 2: $\geq 40\%$ and $\leq 69\%$ ;

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  Group 3: $\leq 39\%$.

The tag belongs to group 1 when its weight is 70 higher than the others. For a recommendation is based on that group, at least four tags extracted from a book or document must be identical to the group’s tags 1. If this condition is not satisfied, the extracted tag will be analyzed based on the group 2, in this case at least 5 tags must be compatible. Also, in group 3 at least 5 tags that are compared must be identical to have some recommendation. The amount of tags and percentage of groups were defined observing the precision and recall values, obtaining better results in the experiments. So, the algorithm was run repeatedly varying the percentage criteria of groups and number of tags to identify the best cutoff values.

Two strategies are used to trace the user profile: mapping of the historical loans and collecting feedback. First, the borrowed books metadata are extracted to form a list of tags that will represent their interests. Thus, each user has a list of tags that are used to generate recommendations of books and documents.

In the second stage, a list of items consisting of books and documents is submitted to the users, then, they send feedback of each item according to their interest. The user tags list is changed according to the evaluations. Thus, when the system combines information from the data collected by the feedback and the record loans, it becomes possible to obtain a complete and reliable profile according to the user interest.

The figure 6 shows the screen with the recommendations that the user must evaluate. Each user has a list of personalized recommendations. The access to this information is done through a link that is sent to the email of each user who received the recommendations. The message has a link to access their list of recommendations and information that should be considered to classify items. In this case, the user must evaluate each item a score from 0 to 3, where:

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  Value 0, means that the recommended item does not interest to the user;

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  Value 1, recommended item has a little interest to the user;

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  Value 2, recommended item interests to the user;

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  Value 3, the user has much interest in the recommended item.

If the item is rated with a score 0, we consider that the recommendation should not have been suggested to the user. Thus, we extract the metadata for that item and turn it into tags that are sent to the list of irrelevant user tags. The next recommendation items that have some relationship with these irrelevant tags, will no longer be displayed to the user. At the top of the page, the user has access to a list of relevant and irrelevant tags, and can reallocate them among the lists according to their interest. If the document is recommended from the repository, by clicking on its title, the user is redirected to the page that contains complete information describing in detail the contents of the item to be classified.

The content based filtering approach identifies the user preferably by similar items, which were previously classified as interest. Based on information and specific data about a user, it is possible to identify whether there is any relationship between the user and the content [Valois and Oliveira, 2011]. In our research we considered that the user’s interest is related to the books that were borrowed. Thus, we can identify through the information contained in the books metadata which subjects are interesting to them.

To perform the content filtering, we use the space vector model. With this model it is possible to select an array of items that most resembles a key item. When selecting a desired item, it calculates the similarity among each of the items of these vectors based on the key item [Barragans-Martinez etal., 2010]. In our model, we apply some concepts of this technique to collect information that is important to designate the user’s interest. We extract the textual data of the books’ metadata to generate tags and use the statistical measure TF-IDF (Term Frequency - Inverse Document Frequency) to establish the importance of weights for each tag.

The TF-IDF technique is used in text mining to identify the importance of words in a text. A value is assigned to each term extracted from the text according to the word frequency in the text or in the documents. This value represents the weight that determines the importance of the term in the text, and all documents in the database. The equation (1) shows how is the TF calculation [Elmasri and Navathe and De Oliveira Morais, 2011]:

In this equation the symbols have the following meaning:

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  $TF_{i,j}$ is the frequency of the standard term to term $i$ in the document $D_{i}$.

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  $f_{i,j}$ is the number of occurrences of the word $i$ in document $D_{i}$

The IDF calculation is done as shown in equation (2):

Where:

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  $IDF$ is the inverse of the frequency weight of the document to the term $i$.

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  $N$ is the number of documents in the collection.

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  $n_{i}$ is the number of documents in which the term $i$ occurs.

The TF-IDF uses the product of the normalized frequency of a term $i$ ($TF_{ij}$) in the document $D_{i}$ and the inverse frequency of the term of the document $i$ for determining the weight of a term in the document. The calculation is represented in equation (3):

The frequency utilization only can be misleading because not always the words that are most common are the most important. Therefore, calculating the weight TF-IDF can be useful to determine a more reliable value of relevance of the term in the document.

We adapt this calculation to apply on our data and we consider that a book borrowed by a user is a document and the terms are information extracted from their metadata, such as title, keyword, and author. So, many books represent a set of documents. To calculate the TF-IDF we consider that documents are equivalent to books and their metadata equivalent to terms that are part of a document.

where:

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  $TF_{tl}$ is the frequency of the term normalized to term $t$ in the book $L_{i}$ .

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  $f_{tl}$ is the number of occurrences of the term $t$ in the book $L_{i}$.

To calculate the IDF represented in equation (5) we consider:

where:

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  $IDF_{t}$ it is the inverse of the frequency weight of the book for the term $t$.

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  $N$ is the amount of whole books.

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  $n_{t}$ is the number of books in which the term $t$ occurs.

Thus we calculate the TF-IDF in equation (6):

Describing the adjustment of the formula, the TF is the fraction between the number of times a word occurs in all borrowed books by the total number of terms of the whole books. And the IDF is the fraction of the total number of books by the amount of books that a term occurs. Considering that the user Miguel lent the books “Java Programming”, “Introduction to C++”, and “Computer Networks”. We extract the metadata of these books and get the following information for the books, as table 1:

We use the calculation of the TF-IDF to obtain the weight of each term. The term “object” would have the following results:

In TF calculation, the value 2 is the number of occurrences of the term “object” in any set of terms. The value 9 represents the total number of terms in the set. In the calculation of the IDF, the value 3 is the amount of books of the set, and the value 2 is the number of books in which the term “object” occurs. As a result of TF-IDF calculation we obtain the weight value of 0,333 for the term “objects”.

We adapt the calculation because it usually determines the importance of the term in the document, but in this case, we identify the importance of the term for a particular user. Then, we fill in the user tags list with the relevant weights found for each term.

This technique generates suggestions for particular user opinions and considering similar interests of other users, according to their characteristics or similar behaviors [Kardan and Ebrahimi, 2013]. There are several ways to calculate the similarity, the most used are cosine, Pearson correlation and mean squared difference (MSD). We adopted the similarity cosine because it has been used satisfactorily in several studies [Usharani and Iyakutti, 2013] [De Oliveira and Coello, 2013] [Durao and Dolog, 2010] [Kin etal., 2010] and [Chirita etal., 2007].

A comparative study on similarity metrics was done by [Salazar, 2012] in which two sets of data were integrated, one containing 574 scientific articles and other containing 1771 documents, collected on the web. The metrics were compared and verified for each of the processing time in relation to the size of the string. The metric that had the best time was the similarity of cosine and overlap coefficient, while the SmithWaterman required more time. In equation (7) shows the cosine similarity calculation.

where $A_{i}$ e $B_{i}$ are vector components $A$ and $B$, respectively. The value generated by calculating the similarity cosine is in the range [0,1], where in 1 indicates an exact match between vectors, while 0 indicates the opposite. Therefore, the closer to 1 the result is, greater is the degree of similarity [Zhu etal., 2011].

An important point that should be taken into account in applying this metric is the choice of the amount of similar users to generate recommendations. A very small amount of users may not be enough to generate consistent rules, because the likelihood of these users to classify an item that an individual has an interest is small. However, a high number of users can generalize the rules, due to higher proportion of users who do not resemble the characteristics of others [Sanchez etal., 2008].

In this study we used the calculation of the cosine similarity combined with the TF-IDF calculation to identify users with similar preferences according to the weight of each user tag. Thus, we recommend books that are borrowed by a user, but that may interest the other according to a degree of similarity between them. The calculation of similarity that was used follows the equation (8):

where $U_{x,t}$ is the user tag weight $x$ and $U_{y,t}$ is the user tag weight $y$. By obtaining the similarities between users, an array $U_{x}\times U_{y}$ is formed with values that identify the degree of similarity among users.

To identify the proximity between two users based on their list of tags, consider the following hypothetical situation. User A has in its list the tag X with weight 0.3, the tag Y with weight 0.0 and the tag Z with weight 0.5, while the user B has the tag X with weight 0.5, the tag Y with weight 0.4 and the tag Z with weight 0.3. Each of the tags is part of the user tags list with the weight calculated by the TF-IDF. Applying the values in the similarity formula we have the following results:

The result of the similarity calculation between the user A and user B, resulted in a degree of 0.73 similarity between them. With the entire calculated results the matrix $U_{1}\times U_{1}$ is created.

We use these values similarity among users to indicate books which have a high degree of similarity. In our application, users who have a degree of similarity higher than or equal to 95$\%$ receive the books that were borrowed by them as a recommendation. It is considered a higher percentage to avoid recommendations among users who have little resemblance relationship, less likely to suggest books that are not appropriate to the user profile.

To accomplish this research were collected information from the library management system database and the university’s institutional repository database. The library database contains records of activities held at the library, as well as the history of loans, information about users and books. The data from the institutional repository are digital documents, containing information of articles, dissertations and theses.

To the library database were considered 27,767 loans from 01/01/2014 to 06/01/2015 period in which 1,769 users receive recommendations and 122 people sent their feedback. Many users use personal email, so do not access the link of recommendations list that is sent to the institutional email. In the self-evaluation institutional report developed by Self Evaluation Institutional Commission UFLA - CPA was found that only 54.7$\%$ of students use the institutional email [Scolfro etal., 2016]. The users lack of interest to participate and classify the recommendations also affects data collection.

According to table 3, after the processing phase the amount of recovered tags from the library database decreases to 363,392 and the recovered tags repository after treatment reduce to 495,259, without considering repeated terms.

Users involved in the research are part of 6 different courses: Master in Computer Science, Undergraduation in Computer Science, Information Systems, Administration, Animal Science, and Veterinary Medicine. Purposely, to verify the consistency and to evaluate the algorithm recommendations with more criteria were selected courses that have different content.

The list of recommendations contains 30 items including books and documents for each user to evaluate. A larger number of items can make the evaluation process expensive, causing dropouts at the time of classification.

In recommendation systems, precision metrics, recall and F-Score, they are used to assess if the system recommends items that are actually considered relevant by the user. To enable the calculation of these measures is necessary to quantify and to categorize the items as their information [Lorente etal., 2014]. Items can be classified as relevant or irrelevant and recommended or not recommended, as table 4:

This table uses the same conception of the confusion matrix, where terms like true positive (TP), false positive (FP), false negative (FN), and true negative (VN) are applied to compare specific classification of an item with the classification correct desired, and their values are used in the calculation precision, recall and F-score.

Accuracy is defined by the ratio between the relevant items recommended by recommended items. This metric is used in order to measure the probability of a recommended product to be relevant to the user. The metric is defined in equation (10):

Recall is calculated by the ratio of relevant items recommended by relevant items. Is the probability of a relevant item to be recommended. The metric is defined in equation (11):

F-Score (F) is the harmonic mean combination of precision and recall values. The measure is used to compare different sets of results. The metric is defined in equation (12):

In the following topics, algorithm execution example is described, through all the stages already described above. Then we evaluate the results based on the metrics mentioned in the previous section.

To start the experiments, information from the library database and from the repository are integrated and collected for the user profile construction. The history of user loans in the library is used to generate the users profile from the books metadata extraction. The repository documents, as well as the books are recommended for evaluation in the feedback stage.

In the preprocessing step, the information collected is subject to various filtering treatments to form the lists of tags that are used to generate recommendations. Applies to TF-IDF technique that sets the weights tags to identify the degree of importance of the term for the user. The tag lists of books and the list of documents tags are formed without weights because it is not necessary to identify the importance of the term in the document, but what its importance to the user. In table 5 we present the set of borrowed books of a real user we call UserStudent:

In table 6 we present the set of tags which were taken from the metadata of these books being identified their importance weights defined from the application of TF- IDF calculation:

Table 7 presents the lists of area tags containing the terms of books borrowed in period of a specific course, which in this example is Computer Science. The purpose of this list is to identify the course tags that the user is enrolled according to the loans made in each period.

With the lists of defined tags, the next step is the grouping of this information to generate recommendations. Thus, the tags are separated into three groups according to their weight: the first group is formed by the tags that have higher than or equal to 80$\%$ of the greater weight, the second group the tags that have weight between 79$\%$ and 50$\%$, and the third group the tags weighing less than or equal to 49$\%$. Using groups consider the following criteria to generate recommendations:

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  If at least two user list group 1 tags are equal the book tags, then the book/document is recommended for the user.

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  If there are not equal tags in the group 1, we look in group 2 if there are at least three tags equal to the book to be recommended.

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  If in the set of book/document tags there is no tag in group 1 or group 2, then checks whether the group 3. If at least four book tags correspond to the user’s tags, then the book/document is recommended.

This discretization scheme was used to avoid recommendations that may be irrelevant to the user because a tag that has a lower weight value does not add as much importance as a tag that has a high weight value. Thus, if a recommendation is made considering only a smaller value tag, the probability of irrelevance rating would be higher. Therefore, the tags that have less weight need to be combined to add value on a recommendation.

Then, a list of books and documents is generated as a recommendation for the user to evaluate with a score from 0 (recommendation does not interest) to 3 (recommendation interests). In addition to the recommendations, we calculate the similarity among users, if they have high similarity value (95$\%$), then the books that were borrowed by them will be included in the recommendation list.

Based on feedback, the list of irrelevant tags is formed with the tags of the recommended books/documents, but that the user evaluated with score 0. The user tags list is compared with the list of irrelevant tags, if any irrelevant tag appear on their list will then be removed to avoid irrelevant content recommendations. These steps are repeated in a constant cycle, the more loans and evaluations the user make, the higher the accuracy of the recommendations generated by the system.