Long-tail Session-based Recommendation

• URL: http://arxiv.org/abs/2007.12329v2
• Date: Tue, 4 Aug 2020 02:00:35 GMT
• Title: Long-tail Session-based Recommendation
• Authors: Siyi Liu and Yujia Zheng
• Abstract summary: We propose a novel network architecture, namely TailNet, to improve long-tail recommendation performance. A novel is proposed and applied in TailNet to determine user preference between two types of items, so as to softly adjust and personalize recommendations.
• Score: 7.832914615902803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Session-based recommendation focuses on the prediction of user actions based on anonymous sessions and is a necessary method in the lack of user historical data. However, none of the existing session-based recommendation methods explicitly takes the long-tail recommendation into consideration, which plays an important role in improving the diversity of recommendation and producing the serendipity. As the distribution of items with long-tail is prevalent in session-based recommendation scenarios (e.g., e-commerce, music, and TV program recommendations), more attention should be put on the long-tail session-based recommendation. In this paper, we propose a novel network architecture, namely TailNet, to improve long-tail recommendation performance, while maintaining competitive accuracy performance compared with other methods. We start by classifying items into short-head (popular) and long-tail (niche) items based on click frequency. Then a novel is proposed and applied in TailNet to determine user preference between two types of items, so as to softly adjust and personalize recommendations. Extensive experiments on two real-world datasets verify the superiority of our method compared with state-of-the-art works.

Related papers

• Interactive Visualization Recommendation with Hier-SUCB [52.11209329270573]
  We propose an interactive personalized visualization recommendation (PVisRec) system that learns on user feedback from previous interactions. For more interactive and accurate recommendations, we propose Hier-SUCB, a contextual semi-bandit in the PVisRec setting.
  arXiv  Detail & Related papers  (2025-02-05T17:14:45Z)
• Preference Discerning with LLM-Enhanced Generative Retrieval [28.309905847867178]
  We propose a new paradigm, which we term preference discerning. In preference dscerning, we explicitly condition a generative sequential recommendation system on user preferences within its context. We generate user preferences using Large Language Models (LLMs) based on user reviews and item-specific data.
  arXiv  Detail & Related papers  (2024-12-11T18:26:55Z)
• Preference Diffusion for Recommendation [50.8692409346126]
  We propose PreferDiff, a tailored optimization objective for DM-based recommenders. PreferDiff transforms BPR into a log-likelihood ranking objective to better capture user preferences. It is the first personalized ranking loss designed specifically for DM-based recommenders.
  arXiv  Detail & Related papers  (2024-10-17T01:02:04Z)
• How to Diversify any Personalized Recommender? [0.0]
  We introduce a novel approach to improve the diversity of Top-N recommendations while maintaining accuracy. Our approach employs a user-centric pre-processing strategy aimed at exposing users to a wide array of content categories and topics. Using pre-processed data for training leads to recommender systems achieving performance levels comparable to, and in some cases, better than those trained on original, unmodified data.
  arXiv  Detail & Related papers  (2024-05-03T15:02:55Z)
• A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation [77.42486522565295]
  We propose a novel recommendation approach called LSVCR to jointly conduct personalized video and comment recommendation. Our approach consists of two key components, namely sequential recommendation (SR) model and supplemental large language model (LLM) recommender. In particular, we achieve a significant overall gain of 4.13% in comment watch time.
  arXiv  Detail & Related papers  (2024-03-20T13:14:29Z)
• Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling [66.02953670238647]
  Tail users suffer from significantly lower-quality recommendation than the head users after joint training. A model trained on tail users separately still achieve inferior results due to limited data. We propose a novel approach that significantly improves the recommendation performance of the tail users.
  arXiv  Detail & Related papers  (2022-08-19T02:50:19Z)
• Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation [55.0391061198924]
  Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. It is nontrivial to incorporate price preferences for session-based recommendation. We propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation.
  arXiv  Detail & Related papers  (2022-05-09T10:47:15Z)
• Session-aware Recommendation: A Surprising Quest for the State-of-the-art [6.617487928813376]
  Session-aware recommendations can be personalized according to the users' long-term preferences. We benchmarked recent session-aware algorithms against each other and against a number of session-based recommendation algorithms. Our work indicates that there remains a huge potential for more sophisticated session-aware recommendation algorithms.
  arXiv  Detail & Related papers  (2020-11-06T15:18:01Z)
• Comprehensive Empirical Evaluation of Deep Learning Approaches for Session-based Recommendation in E-Commerce [38.42250061908039]
  In session-based recommendation, a recommendation system counts on the sequence of events made by a user within the same session to predict and endorse other items. We present a comprehensive evaluation of the state-of-the-art deep learning approaches used in the session-based recommendation.
  arXiv  Detail & Related papers  (2020-10-17T17:22:35Z)
• Sequential recommendation with metric models based on frequent sequences [0.688204255655161]
  We propose to use frequent sequences to identify the most relevant part of the user history for the recommendation. The most salient items are then used in a unified metric model that embeds items based on user preferences and sequential dynamics.
  arXiv  Detail & Related papers  (2020-08-12T22:08:04Z)
• A Bayesian Approach to Conversational Recommendation Systems [60.12942570608859]
  We present a conversational recommendation system based on a Bayesian approach. A case study based on the application of this approach to emphstagend.com, an online platform for booking entertainers, is discussed.
  arXiv  Detail & Related papers  (2020-02-12T15:59:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.