Sequential Recommender Systems: Challenges, Progress and Prospects
- URL: http://arxiv.org/abs/2001.04830v1
- Date: Sat, 28 Dec 2019 05:12:28 GMT
- Title: Sequential Recommender Systems: Challenges, Progress and Prospects
- Authors: Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, Mehmet
Orgun
- Abstract summary: 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.
- Score: 50.12218578518894
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The emerging topic of sequential recommender systems has attracted increasing
attention in recent years.Different from the conventional recommender systems
including collaborative filtering and content-based filtering, 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. SRSs involve the above aspects for more precise characterization of
user contexts, intent and goals, and item consumption trend, leading to more
accurate, customized and dynamic recommendations.In this paper, we provide a
systematic review on SRSs.We first present the characteristics of SRSs, and
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.Finally, we discuss the important
research directions in this vibrant area.
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) - Graph and Sequential Neural Networks in Session-based Recommendation: A Survey [41.59094128068782]
Session-based recommendation (SR) specializes in users' short-term preference capture and aims to provide a more dynamic and timely recommendation.
First, we clarify the definitions of various SR tasks and introduce the characteristics of session-based recommendation.
Second, we summarize the existing methods in two categories: sequential neural network based methods and graph neural network (GNN) based methods.
arXiv Detail & Related papers (2024-08-27T08:08:05Z) - 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) - Review-based Recommender Systems: A Survey of Approaches, Challenges and Future Perspectives [11.835903510784735]
Review-based recommender systems have emerged as a significant sub-field in this domain.
We present a categorization of these systems and summarize the state-of-the-art methods, analyzing their unique features, effectiveness, and limitations.
We propose potential directions for future research, including the integration of multimodal data, multi-criteria rating information, and ethical considerations.
arXiv Detail & Related papers (2024-05-09T05:45:18Z) - When Search Meets Recommendation: Learning Disentangled Search
Representation for Recommendation [56.98380787425388]
We propose a search-Enhanced framework for the Sequential Recommendation (SESRec)
SESRec disentangling similar and dissimilar representations within S&R behaviors.
Experiments on both industrial and public datasets demonstrate that SESRec consistently outperforms state-of-the-art models.
arXiv Detail & Related papers (2023-05-18T09:04:50Z) - Sequential/Session-based Recommendations: Challenges, Approaches,
Applications and Opportunities [20.968084179750143]
sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs.
There are many inconsistencies in this area caused by the diverse descriptions, settings, assumptions and application domains.
This work aims to fill in these gaps so as to facilitate further research in this exciting and vibrant area.
arXiv Detail & Related papers (2022-05-22T06:17:36Z) - Advances and Challenges in Conversational Recommender Systems: A Survey [133.93908165922804]
We provide a systematic review of the techniques used in current conversational recommender systems (CRSs)
We summarize the key challenges of developing CRSs into five directions.
These research directions involve multiple research fields like information retrieval (IR), natural language processing (NLP), and human-computer interaction (HCI)
arXiv Detail & Related papers (2021-01-23T08:53:15Z) - Reciprocal Recommender Systems: Analysis of State-of-Art Literature,
Challenges and Opportunities towards Social Recommendation [14.944946561487535]
Reciprocal Recommender System (RRS) is a data-driven personalized decision support tool.
RRS processes user-related data, filtering and recommending items based on the users preferences, needs and/or behaviour.
This paper summarizes the state-of-the-art RRS research to date, focusing on the algorithms, fusion processes and fundamental characteristics of RRS.
arXiv Detail & Related papers (2020-07-17T09:48:46Z) - Deep Conversational Recommender Systems: A New Frontier for
Goal-Oriented Dialogue Systems [54.06971074217952]
Conversational Recommender System (CRS) learns and models user's preferences through interactive dialogue conversations.
Deep learning approaches are applied to CRS and have produced fruitful results.
arXiv Detail & Related papers (2020-04-28T02:20:42Z) - A Survey on Conversational Recommender Systems [11.319431345375751]
Conversational recommender systems (CRS) take a different approach and support a richer set of interactions.
The interest in CRS has significantly increased in the past few years.
This development is mainly due to the significant progress in the area of natural language processing.
arXiv Detail & Related papers (2020-04-01T18:00:47Z)
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.