Quantifying predictability of sequential recommendation via logical constraints

The sequential recommendation is a compelling technology for predicting users’next interaction via their historical behaviors.Prior studies have proposed various methods to optimize the recommendation accuracy on different datasets but have not yet explored the intrinsic predictability of sequential recommendation.To this end,we consider applying the popular predictability theory of human movement behavior to this recommendation context.Still,it would incur serious bias in the next moment measurement of the candidate set size,resulting in inaccurate predictability.Therefore,determining the size of the candidate set is the key to quantifying the predictability of sequential recommendations.Here,different from the traditional approach that utilizes topological constraints,we first propose a method to learn inter-item associations from historical behaviors to restrict the size via logical constraints.Then,we extend it by 10 excellent recommendation algorithms to learn deeper associations between user behavior.Our two methods show significant improvement over existing methods in scenarios that deal with few repeated behaviors and large sets of behaviors.Finally,a prediction rate between 64%and 80%has been obtained by testing on five classical datasets in three domains of the recommender system.This provides a guideline to optimize the recommendation algorithm for a given dataset.

Multimodal Interactive Network for Sequential Recommendation

Building an effective sequential recommendation system is still a challenging task due to limited interactions among users and items.Recent work has shown the effectiveness of incorporating textual or visual information into sequential recommendation to alleviate the data sparse problem.The data sparse problem now is attracting a lot of attention in both industry and academic community.However,considering interactions among modalities on a sequential scenario is an interesting yet challenging task because of multimodal heterogeneity.In this paper,we introduce a novel recommendation approach of considering both textual and visual information,namely Multimodal Interactive Network(MIN).The advantage of MIN lies in designing a learning framework to leverage the interactions among modalities from both the item level and the sequence level for building an efficient system.Firstly,an item-wise interactive layer based on the encoder-decoder mechanism is utilized to model the item-level interactions among modalities to select the informative information.Secondly,a sequence interactive layer based on the attention strategy is designed to capture the sequence-level preference of each modality.MIN seamlessly incorporates interactions among modalities from both the item level and the sequence level for sequential recommendation.It is the first time that interactions in each modality have been explicitly discussed and utilized in sequential recommenders.Experimental results on four real-world datasets show that our approach can significantly outperform all the baselines in sequential recommendation task.

KGSR-GG:A Noval Scheme for Dynamic Recommendation

Recommendation algorithms regard user-item interaction as a sequence to capture the user’s short-term preferences,but conventional algorithms cannot capture information of constantly-changing user interest in complex contexts.In these years,combining the knowledge graphwith sequential recommendation has gained momentum.The advantages of knowledge graph-based recommendation systems are that more semantic associations can improve the accuracy of recommendations,rich association facts can increase the diversity of recommendations,and complex relational paths can hence the interpretability of recommendations.But the information in the knowledge graph,such as entities and relations,often fails to be fully utilized and high-order connectivity is unattainable in graph modelling in knowledge graph-based sequential recommender systems.To address the above problems,a knowledge graph-based sequential recommendation algorithm that combines the gated recurrent unit and the graph neural network(KGSRGG)is proposed in the present work.Specifically,entity disambiguation in the knowledge graph is performed on the preprocessing layer;on the embedding layer,the TransR embedding technique is employed to process the user information,item information and the entities and relations in the knowledge graph;on the aggregation layer,the information is aggregated by graph convolutional neural networks and residual connections;and at last,on the sequence layer,a bi-directional gated recurrent unit(Bi-GRU)is utilized to model the user’s sequential preferences.The research results showed that this newalgorithm performed better than existing sequential recommendation algorithms on the MovieLens-1M and Book-Crossing datasets,as measured by five evaluation indicators.

Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms

Live streaming has grown rapidly in recent years, attracting increasingly more participation. As the number of online anchors is large, it is difficult for viewers to find the anchors they are interested in. Therefore, a personalized recommendation system is important for live streaming platforms. On live streaming platforms, the viewer’s and anchor’s preferences are dynamically changing over time. How to capture the user’s preference change is extensively studied in the literature, but how to model the viewer’s and anchor’s preference changes and how to learn their representations based on their preference matching are less studied. Taking these issues into consideration, in this paper, we propose a deep sequential model for live streaming recommendation. We develop a component named the multi-head related-unit in the model to capture the preference matching between anchor and viewer and extract related features for their representations. To evaluate the performance of our proposed model, we conduct experiments on real datasets, and the results show that our proposed model outperforms state-of-the-art recommendation models.