Seq2Seq models for recommending short text conversations (original) (raw)
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RecSys 2021 Tutorial on Conversational Recommendation: Formulation, Methods, and Evaluation
Fifteenth ACM Conference on Recommender Systems, 2021
Recommender systems have demonstrated great success in information seeking. However, traditional recommender systems work in a static way, estimating user preferences on items from past interaction history. This prevents recommender systems from capturing dynamic and fine-grained preferences of users. Conversational recommender systems bring a revolution to existing recommender systems. They are able to communicate with users through natural language, which enables them to explicitly elicit user preferences by asking whether a user likes an attribute or item or not. Based on information shared through users' responses, a recommender system can produce more accurate and personalized recommendations. We identify five emerging trends in the general area of conversational recommender systems: (1) Question-based user preference elicitation; (2) Multi-turn conversational recommendation strategies; (3) Dialogue understanding and generation; (4) Exploitationexploration trade-offs; and (5) Evaluation and user simulation. This tutorial covers these five directions, providing a review of existing approaches and progress on each topic. By presenting the emerging and promising topic of conversational recommender systems, we aim to provide take-aways to practitioners to build their own systems. We also want to stimulate more ideas and discussions with audiences on core problems of this topic such as task formalization, dataset collection, algorithm development, and evaluation, with the ambition of facilitating the development of conversational recommender systems.
Learning to Ask Appropriate Questions in Conversational Recommendation
Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021
Conversational recommender systems (CRSs) have revolutionized the conventional recommendation paradigm by embracing dialogue agents to dynamically capture the fine-grained user preference. In a typical conversational recommendation scenario, a CRS firstly generates questions to let the user clarify her/his demands and then makes suitable recommendations. Hence, the ability to generate suitable clarifying questions is the key to timely tracing users' dynamic preferences and achieving successful recommendations. However, existing CRSs fall short in asking high-quality questions because: (1) system-generated responses heavily depends on the performance of the dialogue policy agent, which has to be trained with huge conversation corpus to cover all circumstances; and (2) current CRSs cannot fully utilize the learned latent user profiles for generating appropriate and personalized responses. To mitigate these issues, we propose the Knowledge-Based Question Generation System (KBQG), a novel framework for conversational recommendation. Distinct from previous conversational recommender systems, KBQG models a user's preference in a finer granularity by identifying the most relevant relations from a structured knowledge graph (KG). Conditioned on the varied importance of different relations, the generated clarifying questions could perform better in impelling users to provide more details on their preferences. Finially, accurate recommendations can be generated in fewer conversational turns. Furthermore, the proposed KBQG outperforms all baselines in our experiments on two real-world datasets. CCS CONCEPTS • Information systems → Users and interactive retrieval; • Computing methodologies → Knowledge representation and reasoning.
User evaluation of a conversational recommender system
Proceedings of the 4th IJCAI Workshop on …, 2005
This is the fourth workshop on Knowledge and Reasoning in Practical Dialogue Systems. The first workshop was organised at IJCAI-99 in Stockholm, 1 the second workshop took place at IJCAI-2001 in Seattle, 2 and the third workshop was held at IJCAI-2003 in Acapulco. 3 The current workshop includes research in three main areas: dialogue management, adaptive discourse planning, and automatic learning of dialogue policies. Probabilistic and machine learning techniques have significant representation, and the main applications are in robotics and information-providing systems.
Dialogue behavior management in conversational recommender systems
2007
This thesis examines recommendation dialogue, in the context of dialogue strategy design for conversational recommender systems. The purpose of a recommender system is to produce personalized recommendations of potentially useful items from a large space of possible options. In a conversational recommender system, this task is approached by utilizing natural language recommendation dialogue for detecting user preferences, as well as for providing recommendations. The fundamental idea of a conversational recommender system is that it relies on dialogue sessions to detect, continuously update, and utilize the user's preferences in order to predict potential interest in domain items modeled in a system. Designing the dialogue strategy management is thus one of the most important tasks for such systems.
Conversational Recommendation as Retrieval: A Simple, Strong Baseline
Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)
Conversational recommendation systems (CRS) aim to recommend suitable items to users through natural language conversation. However, most CRS approaches do not effectively utilize the signal provided by these conversations. They rely heavily on explicit external knowledge e.g., knowledge graphs to augment the models' understanding of the items and attributes, which is quite hard to scale. To alleviate this, we propose an alternative information retrieval (IR)-styled approach to the CRS item recommendation task, where we represent conversations as queries and items as documents to be retrieved. We expand the document representation used for retrieval with conversations from the training set. With a simple BM25-based retriever, we show that our task formulation compares favorably with much more complex baselines using complex external knowledge on a popular CRS benchmark. We demonstrate further improvements using user-centric modeling and data augmentation to counter the cold start problem for CRSs.
History-Guided Conversational Recommendation
2014
Product recommendation is an important aspect of many e-commerce systems. It provides an effective way to help users navigate complex product spaces. In this paper, we focus on critiquing-based recommenders. We present a new critiquing-based approach, History-Guided Recommendation (HGR), which is capable of using the recommendation pairs (item and critique) or critiques only so far in the current recommendation session to predict the most likely product recommendations and therefore short-cut the sometimes protracted recommendation sessions in standard critiquing approaches. The HGR approach shows a significant improvement in the interaction between the user and the recommender. It also enables successfully accepted recommendations to be made much earlier in the session.
Enhancing Conversational Recommendation Systems with Representation Fusion
ACM Transactions on The Web, 2023
(CRSs) aim to improve recommendation performance by utilizing information from a conversation session. A CRS rst constructs questions and then asks users for their feedback in each conversation session in order to re ne better recommendation lists to users. The key design of CRS is to construct proper questions and obtain users' feedback in response to these questions so as to e ectively capture user preferences. Many CRS works have been proposed; however, they su er from defects when constructing questions for users to answer: (1) employing a dialogue policy agent for constructing questions is one of the most common choices in CRS, but it needs to be trained with huge corpus; (2) it is not appropriate that constructing questions from a single policy (e.g., A CRS only selects attributes that the user has interacted with) for all users with di erent preferences. To address these defects, we propose a novel CRS model, namely, a Representation Fusion-based Conversational Recommendation model (RFCR), where the whole conversation session is divided into two subsessions (i.e., Local Question Search subsession and Global Question Search subsession) and two di erent question search methods are proposed to construct questions in the corresponding subsessions without employing policy agents. In particular, in the Local Question Search subsession we adopt a novel graph mining method to nd questions, where the paths in the graph between users and attributes can eliminate irrelevant attributes; in the Global Question Search subsession we propose to initialize user preference on items with the user and all item historical rating records and construct questions based on user's preference. Then, we update the embeddings independently over the two subsessions according to user's feedback and fuse the nal embeddings from the two subsessions for the * Both authors contributed equally to the paper.
Towards Topic-Guided Conversational Recommender System
Proceedings of the 28th International Conference on Computational Linguistics, 2020
Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. To develop an effective CRS, the support of high-quality datasets is essential. Existing CRS datasets mainly focus on immediate requests from users, while lack proactive guidance to the recommendation scenario. In this paper, we contribute a new CRS dataset named TG-ReDial (Recommendation through Topic-Guided Dialog). Our dataset has two major features. First, it incorporates topic threads to enforce natural semantic transitions towards the recommendation scenario. Second, it is created in a semi-automatic way, hence human annotation is more reasonable and controllable. Based on TG-ReDial, we present the task of topic-guided conversational recommendation, and propose an effective approach to this task. Extensive experiments have demonstrated the effectiveness of our approach on three sub-tasks, namely topic prediction, item recommendation and response generation. TG-ReDial is available at https://github.com/RUCAIBox/TG-ReDial.
2021
The development of dialogue systems benefits from the study of the communication strategies used by human speakers. In the context of recommendation dialogue systems some researchers have investigated the sociable recommendation strategies employed by the Recommenders in natural settings to make successful and persuasive recommendations (Hayati et al., 2020, INSPIRED corpus). However, the Seeker’s contribution, as well as the Recommender’s, shapes the development of the communicative exchange, in that the Seekers may use specific strategies to disclose their preferences and reach their goal. So, modelling the Seeker’s communicative strategies along with the ones used by the Recommender may improve the efficiency of recommendation dialogue systems. In this work, we provide a reliable tagset for the Seekers utterances present in the Inspired dataset, defining a set of communicative strategies coherent with the already existing one for the Recommenders.