3.3.1. Recommender systems

Due to the huge amount of resources accessible via the Internet, finding relevant information becomes a challenging task. As a response, recommender systems (RSs) have been introduced to achieve personalization and increase users’ satisfaction by recommending items that fit their needs and tastes. These systems have been widely used in various domains and diverse applications and have drawn increasing attention from different research communities such as machine learning, electronic commerce and information retrieval. The main task of recommender systems is to analyze as much as possible users’ feedback in order to learn the users’ preferences. RSs rely on different types of input data such as:

– explicit feedback, which is the information we obtain by directly questioning the user about the proposed items;

– implicit feedback, which is the information we obtain by analyzing the behavior of the user such as clicks/queries/watches.

The output of RS can be twofold (Vozalis and Margaritis 2003):

– a prediction expressed as a numerical value,  which represents the anticipated opinion of active user  for item  This predicted value should necessarily be within the same numerical scale (e.g. 1 – bad and 5 – excellent) as the input referring to the opinions provided initially by active user  This form of recommendation system’s output is also known as individual scoring;

– a recommendation expressed as a list of  items, where  which the active user is expected to like the most. The usual approach in that case requires this list to include only items that the active user has not already purchased, seen or rated. This form of recommendation system’s output is known as top-N recommendation or ranked scoring.

The use of efficient and accurate recommendation methods is very important for a system that will provide good and useful recommendations to its users. This process explains the importance of understanding the features and the potentials of the different recommendation approaches. The recommendation methods are classified into three main classes, including content-based, collaborative filtering and hybrid. First, the collaborative filtering approach (Breese et al. 1998) relies on a matrix of user-item ratings to predict unknown matrix entries, and thus to decide which items to recommend. There are two main CF approaches: item-based (Sarwar et al. 2001), where the recommendations are generated based on similar items that the active user has liked in the past, and user-based (Shardanand, and Maes 1995), where the recommendations are computed based on the opinions of similar users. Second, the content-based approach (Soboroff and Nicholas 1999) uses the contents of the items that the active user has liked in the past and suggests items having similar features to them. And third, the hybrid approach (Chu and Tsai 2017) combines content-based filtering and collaborative filtering in order to take advantage of the representation of the content and the rating information of users to produce recommendations.

Most of the RS algorithms consider the quantitative preferences of users, which are generally expressed on a numerical scale. However, the quantitative notes may not be consistent. In fact, they can be affected by many key factors including the user’s mood when one user may react differently with the same item according to their situation, the limited scale when the user may give the same rating to two items that they appreciate differently since the scale of possible values is generally reduced, etc. Thus, numerical data of users are not reliable and cannot represent the precise degree of users’ liking. For these reasons, many works highlight the use of textual data as it may be a strong support to consider in the recommendation process.

3.3.2. Text mining-based recommendation systems

Text mining is a process to extract interesting and significant patterns to explore knowledge from textual data sources. It is a multidisciplinary field based on information retrieval, data mining, machine learning, statistics, and computational linguistics (Talib et al. 2016). Several works have used the text mining techniques in  to analyze the users’ personal reviews and online behavior and capture their interests. For instance, Li et al. (2015) proposed an  based on opinion mining. The idea consists of taking advantage of the text mining methods in order to detect the opinion-related information from the massive users’ reviews and find the most suitable products for customers. Ziani et al. (2017) presented a basic tool that can be used to analyze Algerian reviews and comments and detect their polarity in order to generate meaningful recommendations for users.

Ganu et al. (2009) proposed a restaurant recommendation system that exploits the users’ comments in order to find out their relative topics and sentiment information. In fact, they used the regression model to estimate scaled sentiment points instead of the two bipolar classes: positive or negative. The authors in Lin et al. (2015) developed a personalized hotel recommendation approach based on both textual and contextual data. They identified the user’s preferred aspects through tracking the browsing behavior on the mobile devices. The authors in Naw and Hlaing (2013) proposed a content-based recommendation method that helps users who want to buy a car by providing them with relevant car information. They implemented a key extraction algorithm to identify the items’ features from the users’ reviews.

All these works have exploited users’ reviews and the textual-related information in order to understand its semantics, and then capture the users’ profiles. However, they are unable to deeply treat the multi-criteria aspect of users’ preferences that is a crucial feature to consider when building user profiles and generating recommendations.

3.3.3. Multi-criteria recommender systems

The multi-criteria aspect is introduced in the context of RS to detect the multi-faceted representation of users’ interests and to correctly build the users’ profiles. In fact, the MCDM methods have been used to make more effective recommendations, in numerous application domains. There are two categories of multi-criteria recommender systems. The first category focuses on using the multi-criteria ratings in order to model a user’s utility for an item as a vector of ratings along several criteria.

For instance, Hdioud et al. (2017) proposed an objective weight determination method called  to determine the criteria with the most important impact on the decision-making process. The main idea consists of computing the overall assessment value of each item as the weighted sum of the performance value over all the criteria; then, they removed one attribute at a time from the set of criteria and considering its correlation with the overall assessment of items without the inclusion of this removed attribute. The alternative with a small correlation should be given a very important weight. In Zheng (2019), the authors developed a utility-based multi-criteria recommender system that computes a utility function of each item based on the multi-criteria ratings. The utility score is defined using the similarity between the vector of the user’s evaluations and the vector of the user’s expectations. In fact, the user’s expectation vector is learned using three optimization learning-to-rank methods (i.e. pointwise ranking, pairwise ranking and listwise ranking).

The second category focuses on analyzing the unstructured textual reviews of users and items’ descriptions to determine the criteria of users’ preferences and to generate recommendations.

In Yashvardhan et al. (2015), the authors implemented a multi-criteria recommendation system for hotel recommendations. The proposed algorithm used various  approaches on a hotel review corpus and the user’s reviews in order to construct a user-item-feature database. The latter is exploited to compute the rating of a hotel from previous users’ preferences with respect to different features. In Yang et al. (2016), the authors proposed a collaborative filtering framework that incorporates both user opinions and preferences on different aspects. The first step consists of analyzing the users’ reviews to extract the opinion words and then computing a score for each aspect. In the second step, the aspect weight is computed using a tensor factorization approach. Finally, the scores and the weights of multiple aspects are used to infer the overall rating, which measures the user’s satisfaction about the item. In Ebadi and Krzyzak (2016), the authors proposed a highly accurate hotel recommender system, which uses the multi-aspect rating system and the large-scale data of different types to suggest hotels for users. In fact, it employs the  and the topic modeling techniques to assess the sentiment of the users’ reviews and extract implicit items criteria. Then, each aspect of the subject problem is treated using a specific sub-recommender system.

Most of these works have shown that when exploiting the textual data to determine the users’ profiles, the recommendation accuracy can be improved. However, most of them focus on the reviews given, one for each specific feature/criteria to infer the criteria weights; then, they use the users’ preferences to compute the overall rating, which cannot accurately reflect the users’ preferences. In fact, the users’ reviews could not contain positive terms/words because they are not completely satisfied by the most interesting criteria for them. Hence, we need to dive deep into the user’s reviews to determine the most interesting criteria for them and to exploit the users’ reviews to capture the item that meets with their expectation.

3.4.1. Primary criterion-based recommendation system

This algorithm is based on two main steps. The first step consists of using the information corpus of each criterion and the active user’s reviews to compute the user’s interests on each criterion. In this algorithm, we will consider just the most interesting criterion for the user, the so-called primary criterion. We assume that by managing only the primary criterion to create the user’s profile, a more focused representation of the user’s preferences is provided.

Formally, given the users’ reviews, we start by putting all the active user’s comments in one sentence denoted as  The  is defined as the union of all the active user’s reviews. Formally,  is:

[3.3]

where  is the number of the active user’s reviews and  is the current review.

Then, to find the primary criterion of the active user, we compute the percentage of the existence of each criterion  in the active user’s reviews denoted by  The criterion with the highest  value is selected as the primary criterion of the active user. Formally,  is:

[3.4]

where  is the number of criteria,  is the current criterion and   is the information corpus of the current criterion.

The second step consists of analyzing the users’ reviews of all the unseen items to capture the ones that may satisfy the primary criterion of the active user. Formally, let  be the number of all the items not yet seen by the active user. Given the users’ reviews, we put all the comments of each unseen item in one sentence denoted as  which is defined as the union of all the reviews given to item  Formally,  is:

[3.5]

where  is the number of all the reviews to item  and  is the  review to item 

Finally, to find out the items matching the primary criterion of the active user, we compute the percentage of each criterion i in the review content of the unseen item  denoted by  Formally,  is:

[3.6]

where  is the number of criteria,  is the current criterion and   is the current unseen item.

The items having the highest IPEC value with the active user’s primary criterion are then selected for the recommendation list. To better explain this, we have summarized the process of this solution in Figure 3.1.

Algorithm 3.1 presents the pseudocode of the primary criterion-based recommendation system algorithm.

The next step consists of analyzing the  which is defined as the union of all the active user’s reviews. Table 3.1 shows all the reviews of Anthony Rodreguez. The  of criterion service (respectively service, price, view and ambiance) is 22% (respectively 8%, 6%, 3% and 3%). Thus, the primary criterion for Anthony is the food quality. In the last step, we compute the  of each unseen item and we recommend to Anthony “212 steak house” and “kobe beef” restaurants, as the users were very satisfied with their food quality.

3.4.2. Multi-criteria text mining-based recommendation system

The multi-criteria text mining-based recommendation system uses the text mining techniques on both the active user’s reviews to detect the weight of importance of each criterion and the users’ reviews of each unseen item to capture the score of satisfaction of users for each criterion. Then, a multi-criteria decision-making method is applied to compute the overall rating for all the unseen items with respect to multi-criteria preferences. The items with the highest ratings are returned to the user. In this work, we use the weighted sum method  as it is the most commonly used multi-criteria decision-making method.

Algorithm 3.2 presents the pseudo-code of the multi-criteria text mining-based recommendation system algorithm.

3.5.1. Dataset and metrics

TripAdvisor is an American website that offers tourists advice from consumers on hotels, restaurants, cities and regions, places of leisure, etc. It also provides accommodation and airline ticket booking tools that compare hundreds of websites to find the best deals. Each week hundreds of users visit TripAdvisor to rate and receive recommendations for new restaurants. The users’ preferences can be expressed in two forms: the explicit ratings and the unstructured textual reviews.

We start by using the WebCrawler technique to extract the data used to test the performance of our proposed algorithms. In fact, this technique is generally designed to explore the Web. This robot is generally designed to collect resources (e.g. web pages, images, videos, Word documents, PDF, etc.) and allows a search engine to index them.

The extracted data is saved in an Excel file. In the following, we present the textual and the statistical description of the data:

1) Textual description of the database: the database extracted is a corpus of comments on restaurants in the Lille region. This dataset contains 47,168 comments and ratings collected from 10,000 users of the site on 1,000 restaurants, from 18/11/2007 to 01/11/2016. Five criteria are considered, including food, service, ambience and price. The rating is given in five discrete levels from 1 to 5.

2) Statistical description of the database: a statistical description allows us to better analyze the database by providing statistics that can enable us to better align our contribution. Table 3.2 presents a statistical description of the TripAdvisor dataset.

The dataset has been divided into two subsets: training set and testing set. In the training set, we are interested in the users’ reviews to detect the primary criterion of the active user, detect the items that match this criterion and finally generate recommendations. The testing set is used to evaluate the effectiveness of the generated recommendations with regard to the real defined rating of the user. To this end, we first split the data chronologically into testing (20% of the recent instances) and training set (the remaining old instances).

3.5.2. Evaluation metrics

Recommendation systems research has used several types of measures for evaluating the quality of the provided recommendation. The evaluation metrics can be categorized into three classes, including predictive accuracy metrics, classification accuracy metrics and rank accuracy metrics (Herlocker et al. 2004). In this work, we are interested in classifying metrics because they are most commonly used in natural language processing and easier to interpret directly (Herlocker et al. 2004). In fact, these metrics measure how many times a recommendation system makes correct or incorrect decisions about whether an item is good. Table 3.3 shows the possible categorization of items, where N is the number of items in the database and how the items are categorized.

	Suggested	Non-suggested	Total
Relevant
Irrelevant
Total

We can conclude that recommended items can be either successful recommendations (i.e. relevant) or unsuccessful recommendations (i.e. non-relevant) and the relevant items can be either suggested in the recommendation list or not. Precision, recall and F-measure (Basu et al. 1998) are the most popular metrics for evaluating information retrieval systems.

– Precision is used to evaluate the validity of a given recommendation list, and it is defined as the ratio of relevant items selected by the active user relative to the number of items recommended to them:

[3.7]

– Recall computes the portion of favored items that were suggested for the active user relative to the total number of the objects actually collected by them:

[3.8]

– F-measure is a measure of a statistic test accuracy. It considers both precision and recall measures of the test to compute the score. We could interpret it as a weighted average of the precision and recall, where the best F-measure has its value at 1 and worst score at the value 0:

[3.9]

3.5.3. Experimental protocol

We used the text mining techniques to clean and transform the users’ reviews as follows:

1) eliminate special characters from user comments;

2) create a list of stop words and add an extension. We use the latter to eliminate the stop words from the comments of the users;

3) concatenate all the comments of a single user/item in one sentence;

4) use the tokenization library to have a list of words that builds a sentence;

5) use the stemmatization library to extract the origin of the words from the obtained sentence.

Once the comments are extracted and cleaned, we move to the exploitation step, which consists of determining the user’s interests on the different criteria by computing how many words and terms contain the reviews from each criterion corpus.

We choose to compare our proposed algorithms with the multi-criteria algorithms, including the multi-criteria weighted average-based recommendation system (Lee and Teng 2007) that uses users’ ratings in the multi-criteria aspect to provide a recommendation and reviews the similarity-based recommendation system (Naw and Hlaing 2013) that uses text mining techniques and the Jaccard similarity measure in order to find similarity between reviews to make the recommendation. To go further, we perform an experiment to test the effect of the sparse data on the effectiveness of all the above algorithms. We tested with different numbers of comments that each user has made (i.e. 3, 4 and more than 4 comments). The results of the different algorithms are listed in Tables 3.4 and 3.5.

3.5.4. Experimental results

It can be noted from Table 3.4 that the recommendations performance of the criterion’s weighted average-based recommendation system is the worst. In fact, the users should give ratings for each feature of the item regardless of whether they are interested in the features or otherwise. Moreover, the numeric scale cannot be very reliable since it can be interpreted differently. This confirms what is stated in Lee and Teng (2007). Researchers note that the algorithm that is based on computing the average of the criteria is defined as a traditional way to rank multiple items.

Comparing the reviews similarity-based recommendation system with the criterion’s weighted average-based recommendation system, we record an improvement that can be explained by the potential of the textual data (e.g. opinions and reviews) in the users’ preferences determination.

Table 3.4 shows that this algorithm has the best results regarding the F-measure evaluation metric than all the other algorithms. This improvement is due to the exploitation of the unstructured textual data not only to detect the active user’s interests on different criteria but also to detect the items that satisfy the multi-criteria preference of the active user. This can only be a good sign towards the use of text mining techniques and multi-criteria decision-making aspect. Table 3.5 shows that the greater the number of reviews to be analyzed for a user, the better the quality of the recommendation. We could mention that the algorithm multi-criteria text mining-based recommendation system is always the most efficient even with sparse data.

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