MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation

• URL: http://arxiv.org/abs/2304.08382v1
• Date: Mon, 17 Apr 2023 15:49:34 GMT
• Title: MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation
• Authors: Kibum Kim, Dongmin Hyun, Sukwon Yun, Chanyoung Park
• Abstract summary: The long-tailed problem is a long-standing challenge in Sequential Recommender Systems (SRS) We propose a novel framework for SRS, called Mutual Enhancement of Long-Tailed user and item (MELT) MELT jointly alleviates the long-tailed problem in the perspectives of both users and items.
• Score: 8.751117923894435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The long-tailed problem is a long-standing challenge in Sequential Recommender Systems (SRS) in which the problem exists in terms of both users and items. While many existing studies address the long-tailed problem in SRS, they only focus on either the user or item perspective. However, we discover that the long-tailed user and item problems exist at the same time, and considering only either one of them leads to sub-optimal performance of the other one. In this paper, we propose a novel framework for SRS, called Mutual Enhancement of Long-Tailed user and item (MELT), that jointly alleviates the long-tailed problem in the perspectives of both users and items. MELT consists of bilateral branches each of which is responsible for long-tailed users and items, respectively, and the branches are trained to mutually enhance each other, which is trained effectively by a curriculum learning-based training. MELT is model-agnostic in that it can be seamlessly integrated with existing SRS models. Extensive experiments on eight datasets demonstrate the benefit of alleviating the long-tailed problems in terms of both users and items even without sacrificing the performance of head users and items, which has not been achieved by existing methods. To the best of our knowledge, MELT is the first work that jointly alleviates the long-tailed user and item problems in SRS.

Related papers

• Large Language Model Empowered Embedding Generator for Sequential Recommendation [57.49045064294086]
  Large Language Model (LLM) has the potential to understand the semantic connections between items, regardless of their popularity. We present LLMEmb, an innovative technique that harnesses LLM to create item embeddings that bolster the performance of Sequential Recommender Systems.
  arXiv  Detail & Related papers  (2024-09-30T03:59:06Z)
• LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation [58.04939553630209]
  In real-world systems, most users interact with only a handful of items, while the majority of items are seldom consumed. These two issues, known as the long-tail user and long-tail item challenges, often pose difficulties for existing Sequential Recommendation systems. We propose the Large Language Models Enhancement framework for Sequential Recommendation (LLM-ESR) to address these challenges.
  arXiv  Detail & Related papers  (2024-05-31T07:24:42Z)
• Alleviating the Long-Tail Problem in Conversational Recommender Systems [72.8984755843184]
  Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations. Existing CRS datasets suffer from the long-tail issue, ie a large proportion of items are rarely (or even never) mentioned in the conversations. This paper presents textbfLOT-CRS, a novel framework that focuses on simulating and utilizing a balanced CRS dataset.
  arXiv  Detail & Related papers  (2023-07-21T15:28:47Z)
• Multimodal Recommender Systems: A Survey [50.23505070348051]
  Multimodal Recommender System (MRS) has attracted much attention from both academia and industry recently. In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views. To access more details of the surveyed papers, such as implementation code, we open source a repository.
  arXiv  Detail & Related papers  (2023-02-08T05:12:54Z)
• Sequential Search with Off-Policy Reinforcement Learning [48.88165680363482]
  We propose a highly scalable hybrid learning model that consists of an RNN learning framework and an attention model. As a novel optimization step, we fit multiple short user sequences in a single RNN pass within a training batch, by solving a greedy knapsack problem on the fly. We also explore the use of off-policy reinforcement learning in multi-session personalized search ranking.
  arXiv  Detail & Related papers  (2022-02-01T06:52:40Z)
• Learning Complex Users' Preferences for Recommender Systems [0.5076419064097734]
  This thesis focuses on improving the performance of general RSs. Most of the existing general RSs tend to exploit the users' rating patterns on common items to detect similar users. We propose a personality-based RS in which similar users are identified based on the similarity of their personality traits.
  arXiv  Detail & Related papers  (2021-07-04T03:25:15Z)
• Dynamic Embeddings for Interaction Prediction [2.5758502140236024]
  In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention. Recent studies have shown the effectiveness of modeling the mutual interactions between users and items using separate user and item embeddings. We propose a novel method called DeePRed that addresses some of their limitations.
  arXiv  Detail & Related papers  (2020-11-10T16:04:46Z)
• Sequential Recommender Systems: Challenges, Progress and Prospects [50.12218578518894]
  sequential recommender systems (SRSs) try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time. We first present the characteristics of SRSs, then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic.
  arXiv  Detail & Related papers  (2019-12-28T05:12:28Z)

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.