CESRec: Conversational Sequential Recommendation

• CESRec is a framework that integrates sequential and conversational recommendation by transforming user interaction histories using conversational feedback.
• It employs dual alignment outlier items masking and semantic pseudo sequence construction to refine historical data for improved recommendation accuracy.
• The model-agnostic design enhances systems like SASRec and LLaRA, demonstrating significant performance gains across datasets with clear empirical improvements.

Searching arXiv for CESRec and closely related recommendation papers to ground the article. arxiv_search query: (Wang et al., 11 Sep 2025) CESRec sequential recommendation conversational feedback CESRec, short for Constructing Pseudo Interactions for Sequential Recommendation via Conversational Feedback, is a framework for integrating sequential recommendation systems (SRS) and conversational recommendation systems (CRS) by using conversational feedback to rewrite a user’s historical interaction sequence into a pseudo-interaction sequence that is then consumed by a standard sequential recommender (Wang et al., 11 Sep 2025). Its central premise is that SRS captures long-term behavioral regularities from historical interactions, whereas CRS elicits immediate preference signals through natural-language interaction; CESRec treats these signals as complementary rather than separate, and operationalizes the combination through two main components: Dual Alignment Outlier Items Masking and Semantic Pseudo Sequence Construction.

1. Position within sequential and conversational recommendation

CESRec is situated at the intersection of two recommendation paradigms with distinct strengths and limitations. In the formulation adopted by the paper, conventional SRS methods are effective at modeling long-term preferences from historical clicks or interactions, but they often rely on collaborative filtering signals and sequence patterns that do not adequately capture changing or immediate interests. CRS, by contrast, is effective at eliciting real-time user intent through dialogue, but typically does not exploit the user’s historical interaction sequence in a comparably direct way (Wang et al., 11 Sep 2025).

The framework is motivated by the observation that conversational signals should affect the sequence representation itself rather than merely serve as an auxiliary signal outside the recommender. When a user expresses a preference such as disliking films directed by one director and preferring those by another, CESRec interprets that feedback as evidence that the historical sequence, as seen by the recommender, should be updated. This shifts the role of conversation from side-channel preference elicitation to direct sequence refinement.

A common misconception is to regard CESRec as a new standalone recommender backbone. The paper instead presents it as explicitly model-agnostic: it is designed to be attached to existing strong SRS models, including SASRec, and also to LLM-based recommenders like LLaRA. In this sense, CESRec is a framework for sequence transformation and preference injection rather than a replacement for sequential recommendation infrastructure.

2. Formal setup and target prediction problem

The paper follows the standard sequential recommendation setup. For a user $u \in \mathcal{U}$, the historical interaction sequence is

$$\mathcal{I}(u) = \{v^{(u)}_1, v^{(u)}_2, \dots, v^{(u)}_{N_u}\},$$

and the task is to predict the next item with which the user will interact (Wang et al., 11 Sep 2025).

CESRec preserves this objective but changes the effective input to the sequential recommender. Rather than predicting from the raw historical sequence alone, the framework constructs a refined pseudo-interaction sequence informed by dialogue. The final recommendation is produced as

$v^{(u)}_{N_u+1}=\text{SRS}(I_{\text{pseudo}(u)}).$

This formulation is significant because it does not require a separate conversational scoring module to override or post-process the sequential model. Instead, the conversational signal is embedded into the representation of user history itself.

The paper also emphasizes that historical sequences often contain outlier items: interactions that are noisy, transient, or otherwise misaligned with the user’s core preferences. Such items can distort sequence reconstruction. CESRec therefore treats the prediction problem as depending on two transformations of the original history: first, removal or masking of items that deviate from core preferences; second, reconstruction of the sequence using conversational feedback.

3. Framework architecture and end-to-end pipeline

CESRec consists of two main components: Dual Alignment Outlier Items Masking and Semantic Pseudo Sequence Construction (Wang et al., 11 Sep 2025). The pipeline proceeds in the following order:

1. Start from the original historical interaction sequence $I(u)$.
2. Use dual alignment to detect and mask outlier items, producing a cleaned sequence $I'(u)$.
3. Obtain user conversational feedback about a recommended item.
4. Feed $I'(u)$ and the feedback into an LLM-based constructor.
5. Generate a pseudo-interaction sequence $I_{\text{pseudo}(u)}$.
6. Feed $I_{\text{pseudo}(u)}$ into the underlying sequential recommender to produce the final recommendation.

This architecture is notable for separating noise suppression from preference injection. The first stage is intended to prevent misleading historical items from influencing the second stage; the second stage then edits the cleaned history so that the sequence reflects both long-term and real-time preferences.

The framework’s model-agnostic character is operational rather than merely rhetorical. The paper applies CESRec to two different backbones, SASRec and LLaRA, indicating that the pseudo-sequence can serve as an intermediate representation for both conventional sequential recommenders and LLM-based recommenders. This suggests a modular design in which CESRec acts as a front-end sequence refinement layer.

4. Dual alignment outlier items masking

The outlier masking module is designed to identify items in the historical sequence that are weakly aligned with the user’s core preferences. For an item $v_i^{(u)}$ with content $c_i$, CESRec first extracts a semantic embedding using an LLM:

$$\mathcal{I}(u) = \{v^{(u)}_1, v^{(u)}_2, \dots, v^{(u)}_{N_u}\},$$0

where the output of the last hidden layer is used as the semantic representation (Wang et al., 11 Sep 2025).

Because semantic embeddings alone do not preserve the collaborative structure of recommendation space, CESRec introduces a trainable adapter that aligns LLM-derived semantics with collaborative representations. The aligned embedding is written as

$$\mathcal{I}(u) = \{v^{(u)}_1, v^{(u)}_2, \dots, v^{(u)}_{N_u}\},$$1

and the appendix gives the adapter explicitly as an MLP:

$$\mathcal{I}(u) = \{v^{(u)}_1, v^{(u)}_2, \dots, v^{(u)}_{N_u}\},$$2

The adapter is trained with the alignment loss

$$\mathcal{I}(u) = \{v^{(u)}_1, v^{(u)}_2, \dots, v^{(u)}_{N_u}\},$$3

so that the resulting representation fuses semantic information from the LLM with collaborative signals from the recommender.

After obtaining hybrid item embeddings for the full sequence, CESRec computes a user representation by mean pooling:

$$\mathcal{I}(u) = \{v^{(u)}_1, v^{(u)}_2, \dots, v^{(u)}_{N_u}\},$$4

where $$\mathcal{I}(u) = \{v^{(u)}_1, v^{(u)}_2, \dots, v^{(u)}_{N_u}\},$$5 is mean pooling. Each item then receives a similarity score

$$\mathcal{I}(u) = \{v^{(u)}_1, v^{(u)}_2, \dots, v^{(u)}_{N_u}\},$$6

with cosine similarity used in the main method. Items with the lowest similarity are treated as outliers, and the top $$\mathcal{I}(u) = \{v^{(u)}_1, v^{(u)}_2, \dots, v^{(u)}_{N_u}\},$$7 lowest-similarity items are masked:

$$\mathcal{I}(u) = \{v^{(u)}_1, v^{(u)}_2, \dots, v^{(u)}_{N_u}\},$$8

with

$$\mathcal{I}(u) = \{v^{(u)}_1, v^{(u)}_2, \dots, v^{(u)}_{N_u}\},$$9

In the main experiments, the paper reports masking one item, while additional analysis examines different numbers of masked items. The appendix compares cosine similarity with Euclidean distance and reports that cosine similarity performs better for identifying outliers and improving recommendation quality. A plausible implication is that angular alignment in the hybrid representation is more useful than raw distance for distinguishing core-preference items from anomalous ones.

5. Semantic pseudo interaction construction

The pseudo-sequence constructor is the mechanism through which conversational feedback is translated into a modified history. CESRec first uses an SRS to recommend an item $v^{(u)}_{N_u+1}=\text{SRS}(I_{\text{pseudo}(u)}).$0 from the current sequence and then collects natural-language feedback about the recommended item’s attributes:

$v^{(u)}_{N_u+1}=\text{SRS}(I_{\text{pseudo}(u)}).$1

A trained LLM, referred to as the Constructor, then rewrites the masked sequence $v^{(u)}_{N_u+1}=\text{SRS}(I_{\text{pseudo}(u)}).$2 into a pseudo-interaction sequence:

$v^{(u)}_{N_u+1}=\text{SRS}(I_{\text{pseudo}(u)}).$3

The intended result is a sequence that reflects both the user’s long-term preference encoded in historical behavior and the user’s real-time preference expressed in dialogue (Wang et al., 11 Sep 2025).

The training objective for the constructor is posed as a sequence prediction problem:

$v^{(u)}_{N_u+1}=\text{SRS}(I_{\text{pseudo}(u)}).$4

Here, $v^{(u)}_{N_u+1}=\text{SRS}(I_{\text{pseudo}(u)}).$5 denotes the optimal pseudo-interaction sequence and $v^{(u)}_{N_u+1}=\text{SRS}(I_{\text{pseudo}(u)}).$6 denotes the constructor model. Supervision is synthetically generated during training by randomly selecting outlier items in a sequence and constructing feedback from the transition between the outlier item and the target item. The constructor is fine-tuned using LoRA.

The key interpretive point is that conversational feedback does not merely modulate scores after sequence encoding; it alters the sequence content itself. The appendix provides examples of how this editing operates. Positive feedback such as “I like comedies” may replace a horror item with a comedy item to amplify comedy recommendations, whereas negative feedback such as “I dislike comedies” may replace a comedy item with a horror item to align the history with an implicit horror preference. CESRec therefore treats conversation as a form of sequence editing, not only as preference labeling.

6. Experimental evaluation and empirical findings

CESRec is evaluated on three datasets: Video Games, Toys, and MovieLens (Wang et al., 11 Sep 2025). The reported statistics are: Video Games with 55,223 users, 17,408 items, and 496,315 reviews; Toys with 208,180 users, 78,772 items, and 1,826,430 reviews; and MovieLens with 6,040 users, 3,883 items, and 1,000,209 reviews. The evaluation metrics are HR@5, NDCG@5, HR@10, and NDCG@10. For evaluation, 100 non-interacted items are sampled and combined with the true next item to form the candidate set.

The main sequential backbones are SASRec and LLaRA. The LLM backbones used for CESRec include LLaMA-2-7b, LLaMA-3-8b, and, in backbone comparison, Qwen2.5-3B-Instruct, Qwen2.5-7B-Instruct, and Mistral-7B-Instruct-v0.3. The user simulator is gpt-4o-mini. Training details reported in the paper include Adam, 200 epochs, learning rate 0.001, and batch size 256 for SASRec; LLaRA ranking uses cosine similarity between item embeddings and output embeddings, following the setup from prior work; CESRec uses LoRA for LLM fine-tuning.

The main results show that SASRec+CESRec and LLaRA+CESRec consistently outperform their respective baselines on all datasets and metrics. Illustrative examples include the following. On Video Games, SASRec improves from HR@5 = 0.590 to 0.646, and NDCG@10 = 0.5042 to 0.5242. On MovieLens, SASRec improves from HR@5 = 0.757 to 0.810, and NDCG@10 = 0.6045 to 0.6244. On Toys, SASRec improves from HR@5 = 0.431 to 0.478, and NDCG@10 = 0.3509 to 0.3659. The paper describes these results as demonstrating state-of-the-art performance by boosting strong SRS models.

Ablation studies isolate the contribution of the two components. CESRec w/o d.a. removes dual alignment masking but retains pseudo-sequence construction; CESRec w/o c. retains masking but removes pseudo-sequence construction. Both modules contribute, but pseudo-sequence construction is reported to contribute more strongly. On Video Games, for example, Full CESRec-LLaMA3 attains HR@5 = 0.646, compared with 0.634 for w/o d.a., 0.610 for w/o c., and 0.590 for the underlying SASRec.

Additional analyses further characterize the framework. Larger LLM backbones tend to perform better, suggesting that stronger semantic understanding improves both masking and sequence construction. CESRec outperforms SASRec across different historical sequence lengths on Toys and Video Games. As the number of user feedback rounds increases, performance steadily improves. The optimal number of masked outliers varies by dataset: MovieLens and Video Games perform best around masking 2 items, whereas Toys improves as more items are masked. Inference latency is dominated by pseudo-sequence construction rather than masking: masking takes about 0.14–0.17s, pseudo-sequence construction takes several seconds depending on the model, and total time is about 4–10s.

7. Interpretation, limitations, and nomenclature

The paper attributes CESRec’s effectiveness to the combination of three elements: long-term behavioral modeling from historical sequences, real-time preference capture from conversational feedback, and noise reduction through outlier masking (Wang et al., 11 Sep 2025). The pseudo-sequence serves as the mechanism that embeds current conversational intent directly into the input consumed by the sequential recommender. This suggests that the framework’s gains are not only due to additional information, but also to the particular representation strategy used to inject that information.

The principal limitation stated explicitly is dependence on clear user feedback. If feedback is vague, ambiguous, or unclear, the constructor may fail to infer real-time preference accurately, and the pseudo sequence becomes less reliable. The future directions suggested in the paper are the design of more sophisticated dialogue mechanisms and methods for helping users express latent preferences more precisely. A plausible implication is that CESRec’s performance ceiling depends not only on the recommender backbone and the constructor LLM, but also on the quality and structure of conversational elicitation.

The term CESRec should also not be conflated with CES, which in a distinct line of work denotes Cloud Energy Storage rather than conversational sequential recommendation. In the energy-systems paper “Selling Renewable Utilization Service to Consumers via Cloud Energy Storage,” CES refers to a third-party-based energy storage sharing model in which an energy storage operator sells renewable utilization service (RUS) to building consumers (Yang et al., 2020). The overlap is nominal only; the two usages belong to different research domains and describe unrelated technical frameworks.