Handling User Cold-Start Problem for Group Recommender System Using Social Behaviour Wise Group Detection Method
Pooja R. Ghodsad, Prashant N. Chatur · 2018
With the accessibility to information, users often face the problem of selecting one item (a product or a service) from a huge search space. This problem is known as information overload. Therefore to help them in searching items on internet we propose a recommender system which recommends items on social networking site considering group members opinion. An important issue for the RS that has greatly captured the attention of researchers is the new user cold-star problem, which occurs when there is a new user that has been registered to the system and no prior rating of this user is found in the rating table. In this paper, items will be recommended to the group of user having similar interest and liking. Thus, the proposed system implements the advanced recommendation model in which users will satisfy in multiple ways. Recommendation will be done in two ways, individual recommendation and group recommendation. Individual recommendation contain preference wise recommendation and group recommendation contain similar profile wise and social behavior wise recommendation. So, both individual as well as group recommendation will be there. So for group recommendation, we propose two automatic group detection method i.e. similar profile wise and social behavior wise. Social behavior wise group detection method is used to avoid the cold-start problem, which also reduce the overhead as well as increase the accuracy. The experimental results indicate that social behavior wise group detection method achieves better accuracy and computation time than the relevant methods. In any case user will get the recommendation as per his likings.