phases of a MCRS. We use different kind of approaches in MCRS to predict good results. The most important and widely used approaches are content-based filtering, CF approach, and knowledge-based approach. We also have a hybrid approach. Now, we will take a deep look about all these approaches of MCRS.
3.3.4 Content-Based Approach
It generates the suitable recommendations for a client that depends on his previous behaviors. It analyzes the user’s previous history like what liked, bought, or watched and accordingly it predicts. It generates a user profile for every user based on their previously selected items and recommends items to him based on similar features items which he liked before. It does not compare his preferences to the users to characterize each user. Content-based filtering approach is divided in three steps which are item representation, learning the user profile, and recommendations generator. In the item representation step, the information or the description of item is extracted to create item’s characteristics. It produces the structured item’s representation. In the next step, a user profile is generated. This user profile is based on the previous behavior such as liking or disliking, the rating or by writing some text comment given by the user for a particular item. This step is known as learning the user profile and the last step is recommendation generator. In this step, a list of recommended items is generated and compared it with the item’s features of the client’s profile. The item that is suitable or most likely is added to the prediction list [1].
This method has been executed in various domains like textual details such as websites, news, and articles and also used for recommending activities such as tourism, travel, TVs, and e-commerce industries. This method works very efficiently if the items size is moderate. This approach relies on content or characteristics of each item so it gives several advantages like it offers a high level personalization in recommendations; it can make its scale up when number of users increases, which means it is scalable. It can make recommendations with a particular interest of a user and it provided a very good security also. These are some advantages of content-based filtering approach [1].
3.3.5 Collaborative Filtering Approach
This technique is immense popular technique among all the multi-criteria recommender system. It interacts with multiple users and generates the recommendation list. If user1 has similarities in their preference with user2, then the item which is recommended to user2 will also be recommended to user 1. The hypothesis behind the following approach is that the clients agreeing other clients in the past will also agree in the future. For a new item, the relationship with user is determined by other users’ review. We can represent as user terms matrix where each cell of the matrix represents the ratings given by clients for a particular item [1].
CF can be divided into two classes: model-based and memory-based. The memory-based approach is a kind of heuristic algorithm. It estimates the item’s rating which depends on another client’s ratings. It can also be classified into two methods: item-based and user-based. The other one, memory-based approach, recommends items based on the similar interests on other users. It analyzes the behavior of other clients like they purchased or liked or viewed before and then recommend the product to this client [1].
CF approaches have many advantages compare to all other filtering approaches like, sometimes, novel and unfamiliar items are recommended, it is very suitable and flexible in various domain, and it does not need to analyze the contents of a particular item [1].
These are some of the advantages of CF in MCRS.
3.3.6 Knowledge-Based Filtering Approach
This is comparatively a new approach than other two approaches. This method is used in those cases where both collaborative- and content-based approaches failed or cannot work properly. The situation happens when there is not enough ratings or reviews are at hand for a particular item for the recommendation process. It is generally happening for those that are hardly ever purchased like houses, cars, or financial services. The way this approach works that it extracts the client’s perception for that domain for recommending the items that will satisfy his requirements the best. The core strength or advantage is that it does not need any previous rating of that problem. By using this approach, it can overcome the cold start problem. But it has a disadvantage also that it needs experienced engineering with all its attendant difficulties to understand the item domain satisfactorily [1].
There is another approach in the recommender system known as the hybrid approach. This approach is made to overcome the limitation of both collaborative and content-based filtering approach. It combines the strength of collaborative and content-based approach they are by combining multiple recommendation algorithm’s implementations into a single recommendation system to improve the efficiency of the recommendation system which, in turn, would show better performance. The hybrid approach is generated by combining two or more algorithms. We must take care of two major points over here. First is keeping an account of the recommendation models that declare the required inputs and the determination of the hybrid recommender system. The second point is determining the strategy that will be used within the hybrid recommender. But there are also certain demerits prevailing in this hybrid approach like it not cost-effective, i.e., it is very expensive to implement because it is an amalgamation of other filtering methods. Moreover, it increases the complexity and, sometimes, needs outside data which is unavailable most of the time [1, 18].
3.4 Comparison Among Different Methods
Now, we will make a comparison between some methods used by researchers around the globe and will see about the result of their research.
3.4.1 MCRS Exploiting Aspect-Based Sentiment Analysis
In this research activity, Musto et al. proposed a CF technique based on MCRS, which utilizes the information to analyze users’ interests conveyed by users’ reviews.
In their experimental data analysis, they use many traditional models for evaluation. The outcomes showed the perception in back of this research [5].
Now, if we look in their experimental data analysis, then we can see that they have used three datasets. Those are Yelp, TripAdvisor, and Amazon.
Table 3.1 Dataset statistics.
| Yelp | TripAdvisor | Amazon | |
|---|---|---|---|
| Users | 45,981 | 536,952 | 826,773 |
| Items | 11,573 | 3,945 | 50,210 |
| Rating/Reviews | 229,906 | 796,958 | 1,324,759 |
| Sparsity | 99.95% | 99.96% | 99.99% |
This framework is mainly for aspect extraction and sentiment analysis.
