Compound critiques for conversational recommender systems (original) (raw)
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Improving recommender systems with adaptive conversational strategies
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Abstract Conversational recommender systems (CRSs) assist online users in their information-seeking and decision making tasks by supporting an interactive process. Although these processes could be rather diverse, CRSs typically follow a fixed strategy, eg, based on critiquing or on iterative query reformulation.
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On the dynamic generation of compound critiques in conversational recommender systems
2004
Conversational recommender systems help to guide users through a product-space towards a particular product that meets their specific requirements. During the course of a “conversation” with the user the recommender system will suggest certain products and use feedback from the user to refine future suggestions. Critiquing has proven to be a powerful and popular form of feedback.
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An Experience-Based Critiquing Approach to Conversational Recommendation
Product recommendation is an important aspect of many e-commerce systems. It provides an effective way to help users navigate complex product spaces. The single-shot approach to product recommendation produces a ranked list of recommendations. This works well when a user is clear about his/her needs, but less so when the needs are not clear or change during a session. In the latter case, it is better for the user to engage in a recommendation dialogue so that the user's incremental feedback can be used to refine their needs and preferences. This type of conversational recommender systems are much better suited for helping users navigate complex product spaces. We focus on critiquing-based recommenders, which allow users to tweak the features of recommended products to refine their needs and preferences. Critiquing-based recommender systems have proven to be an effective approach to conversational recommendation. However they have a tendency to produce protracted recommendation sessions, due to limited feedback that critiques can provide. We describe a novel approach to reusing past critiquing sessions in order to improve overall recommendation efficiency. This new critiquing-based approach is capable of using the current user's critiques so far to predict the most likely product recommendations and therefore short-cut sometimes protracted recommendation sessions in standard critiquing approaches. We demonstrate the potential for using this new technique to improve upon a number of state of the art critiquing techniques. Our approach has the capability of improving both efficiency and quality of recommendation. Recommending users for a new social network user to follow is a topic of interest at present. The existing approaches rely on using various types of information about the new user to determine recommended users who have similar interests to the new user. However, this presents a cold start problem when a new user joins a social network, who is yet to have any interaction on the social network. We present a particular type of conversational recommendation approach, critiquing-based recommendation, to solve the cold start problem. A traditional critiquing-based recommendation system allows a user to critique a feature of a recommended item at a time and gradually leads the user to the target recommendation. However this may require a lengthy recommendation session. Our approaches aim to reduce the session length by taking a case-based reasoning approach. It selects relevant recommendation sessions of past users that match the recommendation session of the current user to short-cut the current recommendation session. It selects relevant recommendation sessions from a case base that contains the successful recommendation sessions of past users. A past recommendation session can be selected if it contains recommended items and critiques that sufficiently overlap with the ones in the current session. Our new techniques show a significant improvement in the interactions between users and recommendation systems. We also show that the new techniques enable satisfactory recommendations made at an earlier stage in the session.
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User Experience and The Role of Personalization in Critiquing-Based Conversational Recommendation
ACM Transactions on the Web
Critiquing — where users propose directional preferences to attribute values — has historically been a highly popular method for conversational recommendation. However, with the growing size of catalogs and item attributes, it becomes increasingly difficult and time-consuming to express all of one’s constraints and preferences in the form of critiquing. It is found to be even more confusing in case of critiquing failures: when the system returns no matching items in response to user critiques. To this end, it would seem important to combine a critiquing-based conversational system with a personalized recommendation component to capture implicit user preferences and thus reduce the user’s burden of providing explicit critiques. To examine the impact of such personalization on critiquing, this paper reports on a user study with 228 participants to understand user critiquing behavior for two different recommendation algorithms: (i) non-personalized , that recommends any item consistent...
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Conversational framework for web search and recommendations
2010
Abstract. In this paper, we describe a Conversational Interaction framework as an innovative and natural approach to facilitate easier information access by combining web search and recommendations. This framework includes an intelligent information agent (Cobot) in the conversation that provides contextually relevant social and web search recommendations.
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