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Diversity plays a crucial role in Recommender Systems (RSs) as it ensures a wide range of recommended items, providing users with access to new and varied options. Without diversity, users often encounter repetitive content, limiting their exposure to ...

Existing Conversational Recommender Systems (CRS) predominantly utilize user simulators for training and evaluating recommendation policies. These simulators often oversimplify the complexity of user interactions by focusing solely on static item ...

Conversational recommender systems (CRSs) aim to capture user preferences and provide personalized recommendations through multi-round natural language dialogues. However, most existing CRS models mainly focus on dialogue comprehension and preferences ...

Conversational recommendation systems elicit user preferences by interacting with users to obtain their feedback on recommended commodities. Such systems utilize a multi-armed bandit framework to learn user preferences in an online manner and have ...

Conversational recommendation system (CRS) is able to obtain fine-grained and dynamic user preferences based on interactive dialogue. Previous CRS assumes that the user has a clear target item, which often deviates from the real scenario. The user may ...

Conversational Recommender Systems (CRSs) leverage natural language dialogues to provide tailored recommendations. Traditional methods in this field primarily focus on extracting user preferences from isolated dialogues. It often yields responses with a ...

Conversational recommendation (ConvRec) systems must understand rich and diverse natural language (NL) expressions of user preferences and intents, often communicated in an indirect manner (e.g., "I'm watching my weight''). Such complex utterances make ...

Research continuously shows that, despite the wide range of skills developed for Intelligent Personal Assistants (IPAs), users tend to engage with only a small number of them. One reason for this discrepancy is the issue of skill discoverability, which ...

Group Decision-Making (GDM) commonly takes place online, e.g., in text-based group chats, for daily tasks like choosing a movie or a restaurant. However, reaching a consensus among members in GDM tasks online is non-trivial due to the high workload of ...

In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions. (1) Data: To gain insights into model behavior in "in-the-wild" ...

Conversational recommender systems (CRS) promise to provide a more natural user experience for exploring and discovering items of interest through ongoing conversation. However, effectively modeling and adapting to users’ complex and changing ...

Recent conversational recommender systems (CRSs) have achieved considerable success on addressing the cold-start problem. While they utilize conversational key-terms to efficiently elicit user preferences, most of them, however, neglect that key-terms ...

Conversational recommender systems (CRS) enhance the expressivity and personalization of recommendations through multiple turns of user-system interaction. Critiquing is a well-known paradigm for CRS that allows users to iteratively refine ...

Explanations in conventional recommender systems have demonstrated benefits in helping the user understand the rationality of the recommendations and improving the system's efficiency, transparency, and trustworthiness. In the conversational environment, ...

Users in consumption domains, like music, are often able to more efficiently provide preferences over a set of items (e.g. a playlist or radio) than over single items (e.g. songs). Unfortunately, this is an underexplored area of research, with most ...

Conversational recommender systems ( CRS s) aim to understand the information needs and preferences expressed in a dialogue to recommend suitable items to the user. Most of the existing conversational recommendation datasets are synthesized or simulated ...

Conversational recommendation systems (CRS) aim to interactively acquire user preferences and accordingly recommend items to users. Accurately learning the dynamic user preferences is of crucial importance for CRS. Previous works learn the user ...

Conversational recommender systems (CRS) explicitly solicit users' preferences for improved recommendations on the fly. Most existing CRS solutions count on a single policy trained by reinforcement learning for a population of users. However, for users ...

This work addresses challenges in developing conversational assistants that support rich multimodal video interactions to accomplish real-world tasks interactively. We introduce the task of automatically linking instructional videos to task steps as "...

Bundle recommender systems recommend sets of items (e.g., pants, shirt, and shoes) to users, but they often suffer from two issues: significant interaction sparsity and a large output space. In this work, we extend multi-round conversational ...