Alleviating the Cold Start Problem in Recommender Systems Based on Modularity Maximization Community Detection Algorithm

Abstract

Recommender system (RS) has become a very important factor in many eCommerce sites. In our daily life, we rely on the recommendation from other persons either by word of mouth, recommendation letters, movie, item and book reviews printed in newspapers, etc. The typical Recommender Systems are software tools and techniques that provide support to people by identifying interesting products and services in online store. It also provides a recommendation for certain users who search for the recommendations. The most important open challenge in Collaborative filtering recommender system is the cold start problem. If the adequate or sufficient information is not available for a new item or users, the recommender system runs into the cold start problem. To increase the usefulness of collaborative recommender systems, it could be desirable to eliminate the challenge such as cold start problem. Revealing the community structures is crucial to understand and more important with the increasing popularity of online social networks. The community detection is a key issue in social network analysis in which nodes of the communities are tightly connected each other and loosely connected between other communities. Many algorithms like Givan-Newman algorithm, modularity maximization, leading eigenvector, walk trap, etc., are used to detect the communities in the networks. To test the community division is meaningful we define a quality function called modularity. Modularity is that the links within a community are higher than the expected links in those communities. In this paper, we try to give a solution to the cold-start problem based on community detection algorithm that extracts the community from the social networks and identifies the similar users on that network. Hence, within the proposed work several intrinsic details are taken as a rule of thumb to boost the results higher. Moreover, the simulation experiment was taken to solve the cold start problem. S. Vairachilai et al.1269

Collaborative Filtering Recommendation Algorithm for Heterogeneous Data Mining in the Internet of Things

“…At present, the mainstream recommendation systems are mainly divided into four categories, that is content-based recommendation, collaborative filtering recommendation, knowledge-based recommendation, and combination recommendation [5], [6]. Among them, the collaborative filtering algorithm can generate recommendations only based on the rating characteristics of similar users or projects, and can discover the potential information needs of the users [7], [8], not requiring the attribute information of the user or the project. And so, it has strong adaptability in different applications and is widely used [9].…”

From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions

“…The strengths of collaborative filtering include its ability to capture complex user preferences and adapt to evolving tastes, solely using the implicit or explicit preferences of users for items captured in a rating or interaction matrix. However, collaborative filtering can face challenges when dealing with sparse data and the cold start problem for new users or items [41,[46][47][48][49]…”