1. Introduction

2. Literature Review and Related Works

2.2. Approaches of News Recommendation System

The usual approaches used in the News Recommendation System (NRS) are three. These basic approaches of recommendation systems are Content-based Filtering, Collaborative filtering and Hybrid (which is a combination of the first two approaches). So, the details of these techniques were discussed.

2.2.1. Content-Based Filtering (CBF)

In our study, we have used different techniques as discussed above. From these techniques, one is the content-based filtering approach. Content-Based Filtering (CBF) is one of the techniques of a news recommendation system, which is based on the properties of the items and tries to recommend news items which are similar to those a given user has liked to read or rated in the past. This method finds the preferences of the current user about the articles of news using the rating history of the current user related to previously used items.

The similarity of items is determined by measuring the similarity in their properties. So, in this type of filtering method, there is no dependency on the rating records of other users in order to generate preferences for current users. Content-based systems focus on the properties of items. For example, if the user has purchased a book on Amazon.com which uses a recommendation system, then the user starts getting additional preferences for buying books from online book store which includes the same or similar keywords information for books.

Therefore, CBF-based recommendation systems generate recommendations using comparative representations of content relating an item to representations of content that are interesting to the user. In this method, news recommendation is done based on the content similarity between the news articles that the user has read, with the newly published news by considering news recent times. As such, many methods have classified this issue as an information retrieval (IR) issue, where content related to the user’s preferred choices is considered an enquiry, and unrated documents are evaluated on the basis of relevance/similarity to this enquiry [12,13,14]. In order to recommend one particular news to a user, the content-based approaches will get the previously rated news based on their category for example, (sports, science & technology, health, business) and then the news with the highest similarity to user preferences are recommended.

2.2.2. Collaborative Filtering (CF)

The other technique used in our study is collaborative filtering. In Collaborative filtering-based RS, the user will be suggested items that people with similar interests and preferences liked in the past. In a CF recommendation system, in order to suggest items to the user, the collaborative filtering recommendation system looks for the peers of the user, that is, a set of users that have similar interests in the item. Then, only the items that are the most liked by the peers of the user would be suggested [4]. So, collaborative filtering is a technology that aims to learn user preferences and make recommendations based on user and community data. It is a complementary technology to content-based filtering (e.g., keyword-based searching). Probably the most well-known use of collaborative filtering has been by Amazon.com for example where a user’s past shopping history is used to make recommendations for new products. However, various approaches to collaborative filtering have been proposed in the past in the research community.

In addition, Collaborative filtering (CF), as a kind of personalized recommendation technique, has been widely used in many domains [15,16]. However, collaborative filtering also suffers from a few issues, for instance, cold start problems, data sparsity, scalability and so on. These problems seriously reduce the user experience. Collaborative filtering recommends items to users according to their preferences. Therefore, a database of user history must be available. However, the database is always very sparse; that is, the user only rates a small number of items. Up until now, there have been many researchers who have focused on prediction accuracy and proposed some solutions.

CF has been becoming popular in both the academy and industry fields with great speed. Despite the overall success of CF systems, they suffer one serious limitation, namely the cold-start problem [5]. This cold-start problem includes two major aspects: new user and new item. Before a recommender system can present a user with reliable recommendations, it should know about this user’s preferences/interests, most likely from a sufficient number of behaviour records, e.g., ratings or log archives [17]. Since collaborative Filtering uses the similarity of users with respect to the items, they rated it is based on both user and item. Collaborative filtering is a technique that uses user-based similarity or item-based similarity. User-based Collaborative Filtering (UBCF) is one of the widely used recommendation technologies, remaining due to its convincing simplicity and quality of recommendations. It starts with the assumption that if a group of users have similar interests in their past, they will have similar interests in other items in the future [18]. The basic idea of User-Based Collaborative Filtering is to find a group of users who have a history of agreeing with an active user (i.e., they either gave similar ratings). Once a neighbourhood of users is identified, feelings from these neighbours are combined to produce recommendations for the active user. Item-based Collaborative Filtering (IBCF) is the similarity between items that are decided by looking at how other users have rated them. And it only considers users who have rated both items for each user and it calculates the difference in ratings for the two items finally, it takes the average of this difference over the users. For example, news in a similar category rated by different readers should be considered as similar items.

2.2.3. Hybrid Approach

In order to exploit the strengths of content-based and collaborative recommendations, one simple method is to accommodate both methods to obtain the two separately-rated lists of recommendations, and then arrive at a final list, which is a merger of the two results. The two predictions employing an adaptive weighted average are combined, where the weight of the collaborative component increases in line with the increase in the number of users accessing an item [19,20]. In this study, a hybrid approach is used which contains both collaborative filtering and content-based techniques.

The single recommendation system approach is not efficient enough to generate relevant and accurate recommendation preferences. So, hybrid recommendation systems came into existence to overcome the limitations of traditional recommendation approaches. The reason why the hybrid approach was selected is that when these two techniques work together it will increase the performance of the system and enhance the capability to answer user’s interest. These systems are based upon combining advantages of more than one traditional approach for recommendation generation for example; collaborative filtering approach with a content-based approach or collaborative filtering with demographic characteristics-based recommendation approach [21]. The Figure shows the Recommendation System Taxonomies.

Figure 1. Recommendation System Taxonomies.

Figure 1. Recommendation System Taxonomies.

3. Methodology

3.5. Algorithm of the Proposed Model

The algorithm of our proposed system which represent all the approaches we used to overcome the problem formulated in our work is as follows. The pseudo-code consists of each of the separated approaches algorithm and their combination or the hybrid we developed in this study.

Algorithm 3. Pseudo code of Hybrid with Demographic Data Algorithm

4. Result and Discussion

5. Conclusions

6. Future Work

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