DiversityAware Recommendation by User Interest Domain Coverage Maximization Diversityoriented models have been developed to recommend topK items, which utilize some static parameters to make a tradeoff or construct an objective function, derived from item relevance and item diversity. However, such a process directly narrows the interest points of the item list, and would not satisfy usersu0027 preferences very much. Besides, the static parameters mentioned above make recommendation algorithms a lack of adaptability and limit their application scenarios. Aiming at improving the adaptability and efficiency of diversityaware recommendations, we propose a coveragebased approach according to the concepts of usercoverage and usersu0027 interest domain we have defined in this paper. Our method is parameterfree and suitable for either implicit data or explicit data. From a technique perspective, we design an improved greedy algorithm, which is used to achieve user interest domain coverage maximization, and provide solid theoretical proof about performance guarantee on efficiency and recommendation quality. During the experiments, we compare our model against two novel methods on two realworld data sets. Experimental results demonstrate the superiority of our method over the stateoftheart techniques in terms of item relevance and diversity.

Matching Novelty While Training: Novel Recommendation Based on Personalized Pairwise Loss Weighting Most works of recommender system seek to provide highly accurate item prediction while having potentially great bias to popular items. Both users and itemsu0027 providers will suffer if their system has strong preference for monotonous popular items. A better system should consider also item novelty. Previous works of novel recommendation focus mainly on reranking a topN list generated by an accuracyfocused base model. As a result, these frameworks are 2stage and essentially limited to the base model. In addition, when training the base model, the common BRP loss function treats all pairs in the same manner, consistently suppresses interesting negative items which should have been recommended. In this work, we propose a personalized pairwise novelty weighting for BPR loss function, which covers the limitations of BPR and effectively improves novelty with marginal loss in accuracy. Base model will be guided by the loss weights to learn user preference and to generate novel suggestion list in 1 stage. Comprehensive experiments on 3 public datasets show that our approach effectively promotes novelty with almost no decrease in accuracy.