אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Product descriptions play an important role in the e-commerce ecosystem, conveying information to buyers about merchandise they may purchase. Yet, on leading e-commerce websites, with high volumes of new items offered for sale every day, product descriptions are often lacking or missing altogether, and when they do appear, they lack personalization to the user. We suggest to mitigate these issues by generating short crowd-based product descriptions from user reviews and then explore how to personalize them. We first apply an extractive approach, where review sentences are used in their original form to compose the product description. At the core of our method is a supervised approach to identify candidate review sentences suitable to be used as part of a description. Our analysis, based on data from both the Fashion and Motors domains, reveals the top reasons for review sentences being unsuitable for the product's description and these are used, in turn, as part of a deep multi-task learning architecture. We then diversify the set of candidates by removing redundancies and, at the final step, select the top candidates to be included in the description. We compare different methods for each step and also conduct an end-to-end evaluation, based on rating from professional annotators, thus demonstrating the high quality of the generated descriptions. We then address an additional, highly important, component for such descriptions - personalization. Personalization plays a key role in electronic commerce, adjusting the products presented to users through search and recommendations according to their personality and tastes. Current personalization efforts focus on the adaptation of product selections, while the description of a given product remains the same regardless of the user who views it. We propose an approach to personalize product descriptions according to the personality of an individual user. To the best of our knowledge, we are the first to address the problem of generating personalized product descriptions. We first learn to predict a user's personality based on past activity on an e-commerce website. Then, given a user personality, we propose an extractive summarization-based algorithm that selects the sentences to be used as part of a product description in accordance with the given personality. Our evaluation shows that user personality can be effectively learned from past e-commerce activity, while personalized descriptions can lead to a higher interest in the product and increased purchase likelihood.