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СКАЧАТЬ then it also does not have any rating of its quality, durability, etc. This is known as cold start problem. A huge number of research works has done to overcome this this problem [34].

      3.6.2 Sparsity Problem

      This is also a very important problem that all recommender systems are facing nowadays. It happens when user has large history list. The list contains everything like list of items he bought or list of movies he watched or listed of music he listened and even his previous surfing list. Sparsity happens when the user does not give rating after buying items or after watching movies. Rating is the most fundamental element for a MCRS. Because of lack of rating, a recommender system does not able to understand that whether the client liked that particular thing or not [34].

      3.6.3 Scalability

      Scalability is related to the multi-criteria recommender systems own performance. Generally, recommender system does not consume many resources. It is designed such a way that it gives best accuracy by using minimum resource. Recommender system needs to recommend a list of items to the user. But with time, the number of users increases, the number of items also increases, and the recommendation list is also increasing [34].

      3.6.4 Over Specialization Problem

      When a MCRS is able to know about the choices of a particular client, then it creates a boundary and shows according to their choice without discovering new items and other options with time. This situation is known as over specialization problem [34].

      3.6.5 Diversity

      If a user spends a long time in a platform, then the recommender system has a lot of information about him. If he orders almost same kind of things, then the recommender system generates the recommendation list that is based on the same category only [34].

      3.6.6 Serendipity

      Every recommender system should have an objective that to surprise people by recommending new product and make interest in the user [34].

      3.6.7 Privacy

      For recommender system privacy is very important. As the recommender system knows some information about the user, it should not be going to the outside world. The users need to understand which data is required to approve more ideally items to them and how it used [34].

      3.6.8 Shilling Attacks

      It happens when a user became malicious or unethical. Many times, it is observed that user starts giving false rating in some items. If it happens, then either the rating of that good item will drop down and that item will no longer recommended or a bad quality item will get more good ratings and will be recommended more by MCRS. This is known as shilling attack [34].

      3.6.9 Gray Sheep

      Gray sheep takes place normally in CF systems. Here, the belief of a client does not identify with any group and consequently. It is no able to acquire the advantage of recommendations [34].

3.7 Conclusion

      In this chapter of MCRS, we understood the definition of a recommender system, its growing importance, and applications in various sectors. Further, we discussed about the different phases of a recommender system. Moreover, we deeply analyzed the three different filtering techniques associated with the recommender system that includes collaborative, content-based, and knowledge-based filtering techniques. In addition, we also discussed about the hybrid filtering techniques. To have a better understanding on the concept of the recommender system, we considered five research activities conducted by various researchers across the globe. Lastly, we also discussed about the various advantages and the challenges prevailing in the recommender system.

      Nowadays, recommender systems are widely used in various aspects, with the passage of time, the recommender systems are being modified and are showing better performance. New techniques are implemented in the recommender system to derive a better prediction [1].

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