SISA-Rec: A Semantically Integrated Sequential Recommender with Contrastive Alignment

• URL: http://arxiv.org/abs/2607.11168v1
• Date: Mon, 13 Jul 2026 07:03:51 GMT
• Title: SISA-Rec: A Semantically Integrated Sequential Recommender with Contrastive Alignment
• Abstract summary: We introduce SISA-Rec, a transformer-based framework that embeds semantic context directly into sequential modeling.<n>Our approach fuses item ID embeddings with BERT-based text embeddings via a gated fusion module.<n>A joint learning objective which combines Bayesian Personalized Ranking (BPR) and contrastive alignment loss aligns the underlying behavioral and semantic spaces.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommendation systems help users recommend relevant items from a large collection of choices. Present work on transformer-based sequential recommendation learns user preferences from interaction logs, but it mostly focuses on item identifiers and doesn't fully use the semantic meaning of items. This limitation becomes a major challenge in sparse and cold-start scenarios where historical interaction data is limited. To solve this problem, we introduce SISA-Rec (Semantically Integrated Sequential Recommendation), a transformer-based framework that embeds semantic context directly into sequential modeling. Our approach fuses item ID embeddings with BERT-based text embeddings via a gated fusion module, injects semantic similarity into the self-attention mechanism, and leverages an attention-based aggregation module to construct comprehensive user representations. Finally, a joint learning objective which combines Bayesian Personalized Ranking (BPR) and contrastive alignment loss, aligns the underlying behavioral and semantic spaces. Experiments were conducted on the two highly sparse Amazon Beauty and Amazon Toys \& Games datasets, both having 99.93\% sparsity. The results show that SISA-Rec outperforms state-of-the-art baseline models across all evaluation metrics. Compared with the BERT4Rec \cite{petrov2022systematic}, SISA-Rec improves HR@10 by 16.6\% and NDCG@10 by 10.3\% on Amazon Beauty, and HR@10 by 23.1\% and NDCG@10 by 17.9\% on Amazon Toys \& Games. Cold-start analysis further shows that the proposed model achieves the largest improvements for users with limited interaction historical records. This showcases the value of semantic information when user behavior data is scarce. Overall, the results demonstrate that integrating semantic information into the attention mechanism leads to more accurate and reliable recommendations.

Related papers

• RAGR: Review-Augmented Generative Recommendation [61.29879991713178]
  Review-Augmented Generative Recommendation (RAGR) is a novel GR framework that incorporates review feedback directly into the generative user sequence.<n>RAGR yields consistent and significant gains over strong GR backbones across all metrics.
  arXiv  Detail & Related papers  (2026-05-17T05:21:23Z)
• FedUTR: Federated Recommendation with Augmented Universal Textual Representation for Sparse Interaction Scenarios [58.33672784127872]
  Federated recommendations (FRs) have emerged as an on-device privacy-preserving paradigm.<n>We propose a novel method named FedUTR, which incorporates item textual representations as a complement to interaction behaviors.<n>Our method achieves superior performance, with improvements of up to 59% across all datasets compared to the SOTA baselines.
  arXiv  Detail & Related papers  (2026-01-29T08:55:19Z)
• Latent Geometry of Taste: Scalable Low-Rank Matrix Factorization for Recommender Systems [0.0]
  This work investigates the latent geometry of user preferences using the MovieLens 32M dataset.<n>We demonstrate that constrained low-rank models significantly outperform higher dimensional counterparts in terms of ranking precision.<n>We validate the system's practical utility in a cold-start scenario, introducing a tunable scoring parameter to manage the trade-off between popularity bias and personalized affinity effectively.
  arXiv  Detail & Related papers  (2026-01-06T23:42:40Z)
• Vectorized Context-Aware Embeddings for GAT-Based Collaborative Filtering [0.0]
  This paper presents a Graph Attention Network (GAT) based Collaborative Filtering framework enhanced with Large Language Model (LLM) driven context aware embeddings.<n>Experiments on the MovieLens 100k and 1M datasets show consistent improvements over state-of-the-art baselines in Precision, NDCG, and MAP.<n>Our approach effectively mitigates sparsity and cold-start limitations by integrating LLM-derived contextual understanding into graph-based architectures.
  arXiv  Detail & Related papers  (2025-10-30T13:07:39Z)
• End-to-End Personalization: Unifying Recommender Systems with Large Language Models [0.0]
  We propose a novel hybrid recommendation framework that combines Graph Attention Networks (GATs) with Large Language Models (LLMs)<n>LLMs are first used to enrich user and item representations by generating semantically meaningful profiles based on metadata such as titles, genres, and overviews.<n>We evaluate our model on benchmark datasets, including MovieLens 100k and 1M, where it consistently outperforms strong baselines.
  arXiv  Detail & Related papers  (2025-08-02T22:46:50Z)
• SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding [64.45047674586671]
  We introduce the concept of an intention tree and propose a dataset curation pipeline.<n>We construct a sibling multimodal benchmark, SessionIntentBench, that evaluates L(V)LMs' capability on understanding inter-session intention shift.<n>With 1,952,177 intention entries, 1,132,145 session intention trajectories, and 13,003,664 available tasks mined using 10,905 sessions, we provide a scalable way to exploit the existing session data.
  arXiv  Detail & Related papers  (2025-07-27T09:04:17Z)
• ConsRec: Denoising Sequential Recommendation through User-Consistent Preference Modeling [33.281526528724335]
  We propose the User-Consistent Preference-based Sequential Recommendation System (ConsRec)<n>ConsRec captures stable user preferences and filters noisy items from interaction histories.<n>Results show ConsRec achieves a 13% improvement over baseline recommendation models.
  arXiv  Detail & Related papers  (2025-05-28T08:55:13Z)
• Topology-aware Debiased Self-supervised Graph Learning for Recommendation [6.893289671937124]
  We propose Topology-aware De Self-supervised Graph Learning ( TDSGL) for recommendation. TDSGL constructs contrastive pairs according to the semantic similarity between users (items) Our results show that the proposed model outperforms the state-of-the-art models significantly on three public datasets.
  arXiv  Detail & Related papers  (2023-10-24T14:16:19Z)
• Sequential Recommendation via Stochastic Self-Attention [68.52192964559829]
  Transformer-based approaches embed items as vectors and use dot-product self-attention to measure the relationship between items. We propose a novel textbfSTOchastic textbfSelf-textbfAttention(STOSA) to overcome these issues. We devise a novel Wasserstein Self-Attention module to characterize item-item position-wise relationships in sequences.
  arXiv  Detail & Related papers  (2022-01-16T12:38:45Z)
• Contrastive Self-supervised Sequential Recommendation with Robust Augmentation [101.25762166231904]
  Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. Old and new issues remain, including data-sparsity and noisy data. We propose Contrastive Self-Supervised Learning for sequential Recommendation (CoSeRec)
  arXiv  Detail & Related papers  (2021-08-14T07:15:25Z)
• Dynamic Graph Collaborative Filtering [64.87765663208927]
  Dynamic recommendation is essential for recommender systems to provide real-time predictions based on sequential data. Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations. Our approach achieves higher performance when the dataset contains less action repetition, indicating the effectiveness of integrating dynamic collaborative information.
  arXiv  Detail & Related papers  (2021-01-08T04:16:24Z)

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.