A uri SR ec: Adversarial User Intention Learning in Sequential Recommendation (2024)

Abstract: With recommender systems broadly deployed in various online platforms, many efforts have been devoted to learning user preferences and building effective sequential recommenders. However, existing work mainly focuses on capturing user implicit preferences from historical interactions and simply matching them with the next behavior, instead of predicting user explicit intentions. This may lead to inappropriate recommendations. In light of this issue, we propose the adversarial user intention learning approach for sequential recommendaiton, named AuriSRec. The major novelty of our approach is to explicitly predict user current intentions when making recommendations, by inferring their decision-making process as explained in target reviews (reviews written after interacting with the ground-truth item). Specifically, AuriSRec conducts adversarial learning between an intention generator and a discriminator. The generator predicts user intentions by taking their historical reviews and behavioral sequences as inputs, while target reviews provide guidance. Beyond typical sequential modeling methods in the field of natural language process (NLP), a decoupling-based review encoder and a hybrid attention fusion mechanism are introduced to filter noise and enhance the generation capacity. On the other hand, the discriminator determines whether the intention is generated or real based on their matching degree to the target item, thereby guiding the generator to produce gradually improved intentions. Extensive experiments on five real-world datasets demonstrate the effectiveness of our approach.

## introduction nowadays, recommender systems have become increasingly prevalent in various online platforms. since user behaviors are dynamically evolving over time, the task of sequential recommendation (sr) has received significant attention in the * corresponding author. literature @xcite . representative approaches include early markov chain methods @xcite , and recent advances based on transformer architecture @xcite , which have greatly improved the performance of sequential recommendations. in general, to interact with an item, a user is initially motivated by an intrinsic intention, which contains demands for certain types of items with personal reasons. they then follow the intentions to determine the behavior for subsequent interactions. therefore, an ideal sequential recommender should make recommendations by explicitly inferring user current intentions. however, existing sr models @xcite ) mainly focus on capturing sequential patterns from user historical interactions and simply match them with the next interacted item, without analyzing user explicit intention underlying their interaction behaviors. we argue that this may lead to sub-optimal recommendations. while it is appealing to align recommendation models with the real decision-making process of users, there remains a lack of exploration in this area. one of the main challenges is the difficulty in gathering user explicit intention data for training purposes, as user may not actively disclose this personal information. notably, many e-commerce platforms like amazon and yelp encourage users to write reviews on purchased products, explicitly explaining their experiences in interactions. to make personalized recommendations, we aim to develop explicit intention learning in recommender systems, by employing advanced nlp techniques to analyze user personal information in reviews. specifically, given a target item (i.e., the ground-truth item to recommend), the associated review (called target review) contains explicit evidence of user current intentions, which can guide explicit intention learning. in addition, user historical reviews also reflect their evolving preferences, providing valuable information to predict user in-tentions. despite its promise, there are three main challenges. firstly, predicting user fine-grained intentions is more complex than predicting potential items, posing a challenge to the model's generation capability. secondly, to extract user explicit intention from reviews, existing methods often directly encode review sequences using universal pretrained language models (plms) like bert @xcite . however, we argue that reviews contain complex information, including personalized user preferences foot_0 , general item characteristics, and even noise data @xcite . a sophisticated review encoder is needed to purify these semantics in reviews for effective user intention learning. lastly, both user reviews and behavioral sequences are crucial for intention prediction, but their heterogeneous nature makes it challenging to integrate them directly. to this end, in this paper, we present the proposed adversarial user intention learning approach for sequential recommendation, named aurisrec. our approach takes user historical reviews and behavioral sequences as input, and leverages target reviews as guidance, aiming to explicitly infer user intentions via adversarial learning. to achieve this, we focus on two key points: (1) integrating user historical reviews and behavioral sequences for intention prediction, and (2) aligning the predicted intention with user real intention. for the first point, we design an intention generator by introducing a hybrid attention fusion mechanism. specifically, instead of a universal language model, we equip the generator with a decoupling-based review encoder, to purify user preferences and item characteristics from reviews. then, the generator employs purified user preferences as prompts to enhance behavior sequence encoding and infers preference-enhanced intentions. for the second point, we develop an adversarial learning framework, where user intentions derived from target reviews are treated as real intentions. a discriminator is introduced to evaluate the match between intentions (either real or generated) and target items, guiding the generator to improve intention predictions and recommendations. to our knowledge, this is the first work that explicitly predicts user intention for next interaction. to evaluate the proposed approach, we conduct extensive experiments on five real-world recommen-dation datasets. the results demonstrate that au-risrec outperforms several competing baselines. intention learning for recommendation. this field focuses on inferring user intentions to improve recommendations @xcite . most existing studies predict user intentions in the latent space. especially, dss-rec @xcite introduces an intent variable to capture mutual information between a user's historical interactions and multiple future behaviors. iclrec @xcite clusters user behavior representations and uses the centroids as intention representations. icsrec @xcite splits user behavioral sequences and applies intention learning among the sub-sequences. nevertheless, these studies may introduce biases, as the captured intention is simulated by models implicitly rather than exposed by users. review-based recommendation. as traditional collaborative filtering methods struggle with sparse data @xcite , researchers have explored incorporating reviews to improve modeling @xcite . for sequential recommendation, rns @xcite integrates user historical reviews and behavioral sequences to facilitate recommendations. however, this approach employs a universal review encoder to encode reviews, disregarding their coupled user preferences and item characteristics. moreover, it merely depends on historical reviews to capture user preferences, neglecting user intention expressed in target reviews. in contrast, our work purifies semantics in reviews and explicitly predicts user current intentions, guided by real intentions expressed in target reviews through adversarial learning. ## problem definition in this section, we introduce the notations and define the task. formally, given a set of users u and a set of items i in a recommender system, we can obtain the interaction records for each user @xmath0, and organize them into a chronologically ordered item sequence s @xmath1, i 2 , • • • , i n } with length n. each interaction likely has a corresponding review r written by user u for item i (we also consider cases where reviews are only written after a few interactions). we organize user u's historical reviews into a chronological sequence r @xmath2, r 2 , . . . , r n . especially, we propose inferring user potential intentions by jointly modeling user reviews and historical interaction sequences. to improve their alignments, instead of using item ids, we acquire item textual representations by employing bert @xcite to encode item description text (e.g., title, category, and brand), following a similar approach to @xcite : here we define the task of sequential recommendation. given historical reviews r u and behavioral sequence s u of user u, we aim to recommend a top-k ranking list of items as the potential next items by predicting p(i n+1 |r u , s u ). since the prediction is performed for the (n + 1)-th step, we refer to the ground-truth item i n+1 and its associated review r n+1 as target item and target review, respectively. ## methodology in this section, we present aurisrec, an approach for explicit intention learning. we first describe our overall framework, then discuss two key parts (i.e., review decoupling and intention generation), and finally present the optimization process. figure 1 illustrates the overall architecture. ## conclusion and future work in this paper, we introduce aurisrec, a method for explicit user intentions learning in sequential recommendations. our approach conducts adversarial learning between an intention generator and a discriminator, with user historical reviews and behavioral sequences as inputs, and target reviews as guidance. unlike previous work that takes universal language models for review encoding, we design a decoupling-based review encoder to purify user preferences and item characteristics from historical reviews. the extracted preferences serve as personalized prompts to guide the encoding of user behavioral sequences, thereby generating preference-enhanced intentions. experimental results demonstrate the superiority of our approach. future work will involve using additional data types that reflect user intentions (e.g., ratings), to improve intention predictions.