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This study examines the behavioral aspect of improving the recommendation agent-consumer relationship, utilizing a model of internal information search for unplanned purchases prompted by a recommendation from a collaborative filtering agent. The model describes how consumers update their beliefs about a product upon receiving a recommendation and identifies the factors affecting the increase in the product's expected utility after the recommendation. A Monte Carlo simulation derives propositions regarding how these factors influence the effectiveness of recommendations. Broadly, the marginal value of recommendation depends on the preference structure of the recipient, the attributes of the product on which the recommendation is based, and the characteristics of the population of consumers. The major managerial implication is that retailers should include more information in recommendations when the products are less common or when there is a large variability of user tastes. [ABSTRACT FROM AUTHOR]

Recommender systems persuade as well as recommend. This study investigated some factors that influence the perceived fit of the recommendations a system provides and user perceptions of the preference-elicitation process. The findings indicate that the relevance, transparency, duration, and required effort of the elicitation process are important cues for value, which in turn influences the user's enjoyment of the process and the perceived fit of the recommendation with the user's preferences. [ABSTRACT FROM AUTHOR]

E-commerce recommender systems help consumers to locate products within a complex product-space. Conversational recommender systems engage the user in a multicycle session, suggesting one or more products during each cycle, and using the feedback to inform the suggestions for the next cycle. By combining user feedback over several cycles, the system obtains a clear picture of the product the user wishes to purchase. As demonstrated under several experimental conditions, the performance of recommender systems is dramatically improved by the technique of adaptive selection, which employs critiquing and preference-based feedback, and emphasizes product diversity rather than similarity as a selection constraint. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to automated product recommendation based on the popularity characteristics of products. Popularity plays a significant role in the consumer purchasing process but has not been given much attention in recommendation research. A three-dimensional model of popularity is used to develop popularity classes of products. These are joined with the MovieLens dataset to create a hybrid movie recommendation system that combines genre and popularity information. As compared with collaborative filtering, the hybrid system shows positive results under the conditions of data sparsity and cold-starting. Many interesting issues for further research are suggested. [ABSTRACT FROM AUTHOR]

The complexity of the product assortments offered by on-line selling platforms makes selection a challenging task. Customers differ in respect to expertise and product knowledge, but intelligent recommender systems offer personalized dialogues that support the product-selection process. This paper describes CWAdvisor, a domain-independent, knowledge-based recommender environment that provides users with consistently appropriate solutions, identifies additional selling opportunities, and explains solutions. The discussion uses examples from several application domains to show how model-based diagnosis, personalization, and intuitive knowledge-acquisition techniques support customer-oriented sales dialogues. Experience obtained in industrial projects is reported, and successfully deployed recommender applications are evaluated. [ABSTRACT FROM AUTHOR]