Recommender systems (RSs) are built with the aim to reduce the cognitive load on the user. An e cient RS should ensure that a user spends minimal time in the process. Conversational Case-Based Recommender systems (CCBR-RSs) depend on the feedback provided by the user to learn about the preferences of the user. In our work, we exploit the relationship among the cases/products in addition to the feedback (preference-based feedback (PBF)) provided by the user in several ways to develop an e cient CCBR-RS.
aim of our work is to reduce the cognitive load on the user by reducing the
conversation length. Our work has three broad goals, (a) We deal with pro ling
products based on the feature weights under which a particular product
dominates its competitors and use it to enrich the measure of utility, (b) The second
work identi es the trade-o relationship among products and helps users
identify products that make trade-o s that are in line with their preferences and
(c) We aggregate evidence for each product in the domain through the feedback
provided by the user, the feedback provided on a set of products is propagated
to every product in the domain based on its relationship with them. In each of
these works, we show how our methods help to boost the performance of the
system by empirically evaluating it on three datasets [
Status: Published. \Towards Bridging the Gap between Manufacturer and Users
to Facilitate Better Recommendation" [
The work by Mouli et al. [
As the rst part of our doctoral research, we enrich the utility of the weighted similarity measure (feature weights determined based on CDPM) with additional information from the domain. We call this additional information dominance knowledge. We argue that each product has prospective buyers and the importance that the buyers of a particular product would give to the features of that product would be similar. We make an assumption that the products that are similar to each other are competitors to each other and a prospective buyer of a particular product would prefer it over its competitors. Similar to CDPM we can derive inequalities by comparing every product with its competitors. The solutions to the feature weights are contained in a convex region in the feature weights space. We term this region dominance region of the product. Under the feature weights assumptions from the dominance region of a particular product, the utility of that product is greater or equal to all its competitors. Each product has its own dominance region and each user has their preference region. The dominance region characterises the prospective buyer of the product in terms of feature weights. We de ne a measure of overlap between the dominance region of a certain product and the preference region of the user. We combine the overlap score of a product and its weighted similarity to get the enriched utility of the product. We term our method Predicted Preference Region based Method (PPRM). 2.2
Status: Published. \Why Did Naethan Pick Android over Apple? Exploiting
Trade-o s in Learning User Preferences" [
Given a user query and a product, we can determine the compromises the
users have to make in accepting the product. McSherry [
In our work, we de ne a representation for trade-o s and a similarity measure between a pair of trade-o s. In each interaction cycle, we get the PBF from the user. We compare the PBF with each of the rejected products and determine the trade-o s the user preferred product makes with each of the rejected products. In the next interaction cycle, the usefulness of a product is determined by its similarity with the PBF and the similarity between the trade-o s with the user rejected products. For a product to be included in the recommendation set its similarity with the PBF should be high and the trade-o s it makes with the rejected products should be similar to the trade-o s the PBF makes with the rejected products. 2.3
Status: Accepted. \Show me your friends, I'll tell you who you are:
Recommending products based on hidden evidence" [
Published. \Exploiting the interplay among products for e cient
recommendations" [
We propose a novel view to the process of conversation. The feedback in each interaction cycle is used for aggregating evidence for each product in the domain. The evidence could be both positive and negative. We try to model a sales executive who associates the preferences expressed by a user with some product in the domain and recommends the same to the user. The relation among products both in terms of its MAUT based similarity and its similarity based on trade-o is exploited to propagate positive and negative evidences. In each interaction cycle, one product is preferred and the rest are rejected. A product gets positive evidence proportional to its similarity to PBF and negative evidence proportional to its similarity with the rejected products. We categorize the evidence into dominance based evidence and trade-o based evidence. We discuss how the works from literature can be viewed in the light of evidencebased recommendation and suggest ways in which the propagation of noise in the evidence can be minimised.
In our latest work in [
We evaluate the e ciency of our methods based on leave-one-out methodology
[
We have found our methods to have signi cantly better performance
supporting our hypotheses. The best performance is recorded for the Used Car dataset.
The average reduction in the number of cycles compared to the baseline work
for Used Car dataset for the three methods is as follows. PPRM [
The advantage with CCBR-RSs is its ability to explain why a recommendation is made to the user. We would like to explore the task of explaining recommendations in our approaches.