CESR ec: Constructing Pseudo Interactions for Sequential Recommendation via Conversational Feedback (2025)

Abstract: Sequential Recommendation Systems (SRS) have become essential in many real-world applications. However, existing SRS methods often rely on collaborative filtering signals and fail to capture real-time user preferences, while Conversational Recommendation Systems (CRS) excel at eliciting immediate interests through natural language interactions but neglect historical behavior. To bridge this gap, we propose CESRec, a novel framework that integrates the long-term preference modeling of SRS with the real-time preference elicitation of CRS. We introduce semantic-based pseudo interaction construction, which dynamically updates users’ historical interaction sequences by analyzing conversational feedback, generating a pseudo-interaction sequence that seamlessly combines long-term and real-time preferences. Additionally, we reduce the impact of outliers in historical items that deviate from users’ core preferences by proposing dual alignment outlier items masking, which identifies and masks such items using semantic-collaborative aligned representations. Extensive experiments demonstrate that CESRec achieves state-of-the-art performance by boosting strong SRS models, validating its effectiveness in integrating conversational feedback into SRS.

## introduction sequential recommendation systems (srs) are pivotal in various applications, such as ecommerce @xcite and streaming platforms @xcite , by providing personalized item recommendations based on users' historical interaction sequences @xcite . recently, large language models (llms) have demonstrated remarkable reasoning capabilities @xcite @xcite , making them promising method for enhancing recommendation tasks. several studies @xcite @xcite have demonstrated the superiority of directly applying llms to sequential recommendation tasks. in contrast, conversational recommendation systems (crs) employ natural language interactions to inquire about user preferences and predict personalized item recommendations @xcite @xcite . however, existing srs methods usually rely on collaborative filtering signals while neglecting the rich semantic information associated with items. a significant limitation of these approaches is their inability to capture users' real-time interests, as immediate preferences are not dynamically reflected in the behavior sequence. conversely, while crs methods excel at capturing immediate interests through natural language conversations, they typically fail to incorporate historical interaction sequences into their frameworks @xcite @xcite . consequently, the first challenge lies in dynamically integrating the long-term preference modeling of srs with the real-time interests modeling facilitated by natural language feedback in crs. in this paper, we propose conversation enhanced sequential recommendation (cesrec). to address the first challenge, we introduce semantic-based pseudo interaction construction, a novel method that directly refines the historical interaction sequence based on users' natural language feedback. specifically, this approach analyzes users' natural language feedback to model their current preferences and refines their historical interaction sequence, generating a pseudo-interaction sequence that seamlessly integrates both long-term and real-time preferences. next, we use the pseudointeraction sequence as input to srs, which effectively combines the collaborative filtering signals of srs with the semantic signals derived from natural language feedback. this enables accurate recommendations based on natural language interactions without requiring extensive modifications to existing srs-based systems, ensuring seamless integration and enhanced user experience. since historical interaction sequences often contain items that deviate substantially from users' main preferences, such as mistakenly clicked items or transient interests, as observed in many recent studies @xcite , these outliers can adversely affect the modeling of user behavior. the inclusion of these items can negatively influence the llm's modeling of user behavior, potentially misleading the construction of the pseudo-interaction sequence. for example, if a user's primary preference is horror films, the inclusion of a comedy movie in the interaction sequence might lead the llms to utilize "horror-comedy" films to construct the pseudo-interaction sequence, rather than a pure horror film. in this work, we refer to such items as outlier items. therefore, the second challenge is how to accurately identify these outlier items and mask them in the interaction sequence to minimize their impact on the generation of the pseudo-interaction sequence. to address this, we propose dual alignment outlier items masking, a method that accurately identifies outlier items from the user's historical interaction sequence based on semantic-collaborative aligned representations and subsequently masks these items. specifically, we leverage llms to obtain semantic embeddings of items and extract collaborative representations from the srs model. we then introduce a dual alignment mechanism to derive hybrid item representations, which simultaneously capture co-occurrence relationships and semantic information among items. based on these hybrid representations, we identify items that substantially deviate from the user's core preferences, ensuring precise masking while preserving the integrity of the user's historical behavior sequence. the experimental results demonstrate that our ces-rec can boost the performance of several state-ofthe-art srs models in terms of hr and ndcg, which verifies that our cesrec can effectively integrates long-term preferences with real-time interests through natural language feedback. the main contributions of this work are as follows: llms for recommendation large language models (llms) have demonstrated remarkable capabilities across various domains. recent research explores hybrid approaches to enhance sequential recommendation by integrating traditional recsys with llms @xcite @xcite . @xcite combines id-based embeddings and textual features via hybrid prompting, while @xcite introduces generative retrieval using semantic id decoding. @xcite leverages llms to generate item embeddings, and @xcite aligns id embeddings with text via a projector module. however, they do not fully exploit the rich semantic information contained in users' conversational feedback, limiting their ability to dynamically adapt recommendation strategies based on real-time user preferences. ## problem definition in this paper, we follow the problem definition commonly used in sequential recommendation tasks @xcite . given a user @xmath0, where u represents the set of all users, and a historical interaction sequence nu }, the model aims to predict the next item the user is likely to interact with based on i(u). here, v (u) i denotes the i-th item interacted by user u, and all items belong to the item set v. the sequence length of i(u) is denoted by n u . ## conclusion in this paper, we proposed conversation enhanced sequential recommendation (cesrec), a novel framework that seamlessly integrates the long-term preference modeling of srs with the real-time preference elicitation of crs. by leveraging users' natural language feedback, cesrec dynamically refines historical interaction sequences to generate pseudo-interaction sequences that capture both long-term preferences and real-time interests. additionally, the dual alignment outlier items masking method addresses the challenge of outlier items in historical sequences by accurately identifying and masking items that deviate from users' core prefer-ences. extensive experiments on three real-world datasets demonstrate that cesrec enhances the performance of sota srs models, achieving superior results in terms of hr and ndcg metrics.