Diversity-Ensured Semantic Movie Recommendation by Applying Linked Open Data

Abstract

Abstract:The recommender system becomes a significant research area due to the popularity of the social web. Traditional semantic recommender systems deliver poor performance when balancing the recommendation accuracy and diversity. Also, the rank-based recommendation methods lack to obtain the coverage of the entire preferences of the user in the top-N recommendation list. Thus, this paper presents the Diversity-Ensured Semantic-aware Item REcommendation (DESIRE) that deals with the consistent and reliable knowledge source to significantly improve the quality and provide the diversity-ensured top-N recommendation list. The DESIRE approach builds the semantically relevant graphs such as movie-centric and user rating-centric graph with the help of both the Linked Open Data (LOD) and the explicit ratings of the users. By extracting the semantic-path based features from the user rating-centric graph, it executes the ranking algorithm for the top-N movie recommendation. Moreover, the diversity-aware re-ranking tends to maintain the trade-off between the diversity and accuracy in the top-N recommendation.

Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data

“…With the help of open linked data, it is feasible to provide such services to users (Corsar et al, 2017). The DESIRE recommender system is designed to improve the performance of movie recommendations by keeping both the accuracy and diversity at an optimal level (Srinivasan & Mani, 2018). From the above analysis, it is concluded that none of the existing methods fully utilizes the available semantic information in LOD for cold start and data sparsity problem.…”

Re‐ranking with multiple objective optimization in recommender system

“…When considering the differences in user preferences for different directories, researchers proposed a personalized diverse algorithm based on the greedy re‐ranking strategy to jointly optimize relevance and diversity 7 . The improvement of diversity in re‐ranking is a popular research direction 8‐11 …”