Yasser Salem

Experience-Based critiquing: reusing critiquing experiences to improve conversational recommendation

Product recommendation systems are now a key part of many e-commerce services and have proven to be a successful way to help users navigate complex product spaces. In this paper, we focus on critiquing-based recommenders, which permit users to tweak the features of recommended products in order to refine their needs and preferences. In this paper, we describe a novel approach to reusing past critiquing histories in order to improve overall recommendation efficiency.

History-aware critiquing-based conversational recommendation

In this paper we present a new approach to critiquing-based conversational recommendation, which we call History-Aware Critiquing (HAC). It takes a case-based reasoning approach by reusing relevant recommendation sessions of past users to short-cut the recommendation session of the current user. 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 similar recommended items to the ones in the current session and its critiques sufficiently overlap with the critiques so far in the current session. HAC extends experience-based critiquing (EBC). Our experimental results show that, in terms of recommendation efficiency, while EBC performs better than standard critiquing (STD), it does not perform as well as more recent techniques such as incremental critiquing (IC), whereas HAC achieves better recommendation efficiency over both STD and IC.

CSFinder: A cold-start friend finder in large-scale social networks

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 problem when a new user joins a social network, who is yet to have any interaction on the social network. In this paper we present a particular type of conversational recommendation approach, critiquing-based recommendation, to solve the cold start problem. We present a critiquing-based recommendation system, called CSFinder, to recommend users for a new user to follow. 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. CSFinder aims 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 experimental results show that CSFinder has significantly shorter sessions than the ones of an Incremental Critiquing system, which is a baseline critiquing-based recommendation system.