Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation

• URL: http://arxiv.org/abs/2104.14200v1
• Date: Thu, 29 Apr 2021 08:37:30 GMT
• Title: Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation
• Authors: Junsu Cho, Dongmin Hyun, SeongKu Kang, Hwanjo Yu
• Abstract summary: We propose a novel recommender system for timely recommendations, called TimelyRec. In TimelyRec, a cascade of two encoders captures the temporal patterns of user preference using a proposed attention module for each encoder. Our experiments on a scenario for item recommendation and the proposed scenario for item-timing recommendation on real-world datasets demonstrate the superiority of TimelyRec.
• Score: 15.930016839929047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems have achieved great success in modeling user's preferences on items and predicting the next item the user would consume. Recently, there have been many efforts to utilize time information of users' interactions with items to capture inherent temporal patterns of user behaviors and offer timely recommendations at a given time. Existing studies regard the time information as a single type of feature and focus on how to associate it with user preferences on items. However, we argue they are insufficient for fully learning the time information because the temporal patterns of user preference are usually heterogeneous. A user's preference for a particular item may 1) increase periodically or 2) evolve over time under the influence of significant recent events, and each of these two kinds of temporal pattern appears with some unique characteristics. In this paper, we first define the unique characteristics of the two kinds of temporal pattern of user preference that should be considered in time-aware recommender systems. Then we propose a novel recommender system for timely recommendations, called TimelyRec, which jointly learns the heterogeneous temporal patterns of user preference considering all of the defined characteristics. In TimelyRec, a cascade of two encoders captures the temporal patterns of user preference using a proposed attention module for each encoder. Moreover, we introduce an evaluation scenario that evaluates the performance on predicting an interesting item and when to recommend the item simultaneously in top-K recommendation (i.e., item-timing recommendation). Our extensive experiments on a scenario for item recommendation and the proposed scenario for item-timing recommendation on real-world datasets demonstrate the superiority of TimelyRec and the proposed attention modules.

Related papers

• Sequential Recommendation on Temporal Proximities with Contrastive Learning and Self-Attention [3.7182810519704095]
  Sequential recommender systems identify user preferences from their past interactions to predict subsequent items optimally. Recent models often neglect similarities in users' actions that occur implicitly among users during analogous timeframes. We propose a sequential recommendation model called TemProxRec, which includes contrastive learning and self-attention methods to consider temporal proximities.
  arXiv  Detail & Related papers  (2024-02-15T08:33:16Z)
• Latent User Intent Modeling for Sequential Recommenders [92.66888409973495]
  Sequential recommender models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. Intent modeling is thus critical for understanding users and optimizing long-term user experience.
  arXiv  Detail & Related papers  (2022-11-17T19:00:24Z)
• Everyone's Preference Changes Differently: Weighted Multi-Interest Retrieval Model [18.109035867113217]
  Multi-Interest Preference (MIP) model is an approach that produces multi-interest for users by using the user's sequential engagement more effectively. Extensive experiments have been done on various industrial-scale datasets to demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2022-07-14T04:29:54Z)
• 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)
• Continuous-Time User Preference Modelling for Temporal Sets Prediction [32.35733523016208]
  We propose a continuous-time user preference modelling framework for temporal sets prediction. We first construct a universal sequence by arranging all the user-set interactions in a non-descending temporal order. For each interaction, we continuously update the memories of the related user and elements based on their currently encoded messages and past memories.
  arXiv  Detail & Related papers  (2022-04-12T02:49:27Z)
• A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations [24.815498451832347]
  We present a novel dynamic recommendation model that focuses on users who have interactions in the past but turn relatively inactive recently. Due to the sparse recent interactions, it is challenging to capture these users' current preferences precisely.
  arXiv  Detail & Related papers  (2022-04-03T02:04:12Z)
• Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation [133.8758914874593]
  Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms -- including both shallow and deep ones -- often model such dynamics independently. This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences.
  arXiv  Detail & Related papers  (2022-04-02T03:23:46Z)
• Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation [62.30552176649873]
  We devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe) To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice. To enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices.
  arXiv  Detail & Related papers  (2021-09-24T07:44:27Z)
• Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation [59.183016033308014]
  In this paper, we explore the unique characteristics of the implicit feedback and propose Set2setRank framework for recommendation. Our proposed framework is model-agnostic and can be easily applied to most recommendation prediction approaches.
  arXiv  Detail & Related papers  (2021-05-16T08:06:22Z)
• TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendation [0.0]
  We propose a new Time-aware Long- and Short-term Attention Network (TLSAN) TLSAN learns user-specific temporal taste via trainable personalized time position embeddings with category-aware correlations in long-term behaviors. Long- and short-term feature-wise attention layers are proposed to effectively capture users' long- and short-term preferences for accurate recommendation.
  arXiv  Detail & Related papers  (2021-03-16T10:51:57Z)
• Sequential Recommender via Time-aware Attentive Memory Network [67.26862011527986]
  We propose a temporal gating methodology to improve attention mechanism and recurrent units. We also propose a Multi-hop Time-aware Attentive Memory network to integrate long-term and short-term preferences. Our approach is scalable for candidate retrieval tasks and can be viewed as a non-linear generalization of latent factorization for dot-product based Top-K recommendation.
  arXiv  Detail & Related papers  (2020-05-18T11:29:38Z)

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.