Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts

TL;DR

This paper introduces TAI2Vec, a novel time-aware embedding model that captures user behavior dynamics by integrating personalized temporal contexts into item representations, improving recommendation accuracy.

Contribution

It proposes user-adaptive, temporal-aware embedding strategies that distinguish between session-based and interest drift behaviors, advancing personalized user modeling in recommender systems.

Findings

TAI2Vec outperforms static baselines in 80% of datasets.

Achieves up to 135% improvement in embedding quality.

Demonstrates the effectiveness of personalized temporal segmentation.

Abstract

Effective user modeling requires distinguishing between short-term and long-term preference evolution. While item embeddings have become a key component of recommender systems, standard approaches like Item2Vec treat user histories as unordered sets (bag-of-items), implicitly assuming that interactions separated by minutes are as semantically related as those separated by months. This simplification flattens the rich temporal structure of user behavior, obscuring the distinction between coherent consumption sessions and gradual interest drifts. In this work, we introduce TAI2Vec (Time-Aware Item-to-Vector), a family of lightweight embedding models that integrates temporal proximity directly into the representation learning process. Unlike approaches that apply global time constraints, TAI2Vec is user-adaptive, tailoring its temporal definitions to individual interaction paces. We propose two complementary strategies: TAI2Vec-Disc, which utilizes personalized anomaly detection to dynamically segment interactions into semantic sessions, and TAI2Vec-Cont, which employs continuous, user-specific decay functions to weigh item relationships based on their relative temporal distance. Experimental results across eight diverse datasets demonstrate that TAI2Vec consistently produces more accurate and behaviorally grounded representations than static baselines, achieving competitive or superior performance in over 80% of the datasets, with improvements of up to 135%. The source code is publicly available at https://github.com/UFSCar-LaSID/tai2vec.