User Preference Elicitation, Rating Sparsity and Cold Start

2018 ◽  
pp. 253-294 ◽  
Author(s):  
Mehdi Elahi ◽  
Matthias Braunhofer ◽  
Tural Gurbanov ◽  
Francesco Ricci
Keyword(s):  
Cold Start ◽  
Preference Elicitation ◽  
User Preference

scholarly journals User-Involved Preference Elicitation for Product Search and Recommender Systems

AI Magazine ◽  
2008 ◽  
Vol 29 (4) ◽  
pp. 93 ◽  
Author(s):  
Pearl Pu ◽  
Li Chen
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
Preference Elicitation ◽  
Design Guidelines ◽  
User Preferences ◽  
The State ◽  
User Preference ◽  
Large Collection ◽  
Complete Model ◽  
Product Search

We address user system interaction issues in product search and recommender systems: how to help users select the most preferential item from a large collection of alternatives. As such systems must crucially rely on an accurate and complete model of user preferences, the acquisition of this model becomes the central subject of our paper. Many tools used today do not satisfactorily assist users to establish this model because they do not adequately focus on fundamental decision objectives, help them reveal hidden preferences, revise conflicting preferences, or explicitly reason about tradeoffs. As a result, users fail to find the outcomes that best satisfy their needs and preferences. In this article, we provide some analyses of common areas of design pitfalls and derive a set of design guidelines that assist the user in avoiding these problems in three important areas: user preference elicitation, preference revision, and explanation interfaces. For each area, we describe the state-of-the-art of the developed techniques and discuss concrete scenarios where they have been applied and tested.

scholarly journals A Genetic-Based Pairwise trip Planner Recommender System

2021 ◽  
Author(s):  
Nunung Nurul Qomariyah ◽  
Dimitar Kazakov
Keyword(s):  
Recommender System ◽  
Evaluation Study ◽  
Preference Elicitation ◽  
User Preference ◽  
Elicitation Method ◽  
Massive Growth ◽  
Internet Users ◽  
Pairwise Preference ◽  
Personalized Recommender System ◽  
Trip Planner

Abstract The massive growth of internet users nowadays can be a big opportunity for the businesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user preference elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study.

scholarly journals Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-start Users

2021 ◽  
Vol 39 (4) ◽  
pp. 1-29
Author(s):  
Shijun Li ◽  
Wenqiang Lei ◽  
Qingyun Wu ◽  
Xiangnan He ◽  
Peng Jiang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Cold Start ◽  
User Preference ◽  
Inherent Limitation ◽  
Trade Off ◽  
Thompson Sampling ◽  
Trade Offs ◽  
Benchmark Datasets ◽  
Item Attributes ◽  
Exploration Exploitation

Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Online recommendation, e.g., multi-armed bandit approach, addresses this limitation by interactively exploring user preference online and pursuing the exploration-exploitation (EE) trade-off. However, existing bandit-based methods model recommendation actions homogeneously. Specifically, they only consider the items as the arms, being incapable of handling the item attributes , which naturally provide interpretable information of user’s current demands and can effectively filter out undesired items. In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively. This important scenario was studied in a recent work  [54]. However, it employs a hand-crafted function to decide when to ask attributes or make recommendations. Such separate modeling of attributes and items makes the effectiveness of the system highly rely on the choice of the hand-crafted function, thus introducing fragility to the system. To address this limitation, we seamlessly unify attributes and items in the same arm space and achieve their EE trade-offs automatically using the framework of Thompson Sampling. Our Conversational Thompson Sampling (ConTS) model holistically solves all questions in conversational recommendation by choosing the arm with the maximal reward to play. Extensive experiments on three benchmark datasets show that ConTS outperforms the state-of-the-art methods Conversational UCB (ConUCB) [54] and Estimation—Action—Reflection model [27] in both metrics of success rate and average number of conversation turns.

scholarly journals IMPROVING PERSONALIZED QUESTIONNAIRE WITH REDUNDANCY REDUCTION FOR ADDRESSING COLD USER PROBLEM

2021 ◽  
Vol 5 (1) ◽  
pp. 457-466
Author(s):  
Umar Kabiru ◽  
Abubakar Muhammad
Keyword(s):  
Collaborative Filtering ◽  
Performance Improvement ◽  
Online Shopping ◽  
Cold Start ◽  
Reduction Technique ◽  
User Preference ◽  
Redundancy Reduction ◽  
Significant Performance ◽  
Filtering Techniques

User-based and item-based collaborative filtering techniques are among most explored strategies of making products’ recommendations to Users on online shopping platforms. However, a notable weakness of the collaborative filtering techniques is the cold start problem. Which include cold user problem, cold item problem and cold system problem – i.e., the failure of collaborative filtering to make recommendation of products to a new user, failure of an item to be recommended, or combination of the two respectively.  Literature investigation has shown that cold user problem could be effectively addressed using technique of personalized questionnaire. Unfortunately, where the products’ database is too large (as in Amazon.com), results obtained from personalized questionnaire technique could contain some user preference uncertainties. This paper presents technique of improving personalized questionnaire with uncertainty reduction technique. In addition, the paper presents classification of product recommendation systems. In this work we will be limited to user-based cold start.  Experimentation was conducted using Movielens dataset, where the proposed technique achieved significant performance improvement over personalized questionnaire technique with RMSE, Precision, Recall,1 and NDCG of 0.200, 0.227, 0.261, 0.174 and 0.249

On Design Preference Elicitation With Crowd Implicit Feedback

2012 ◽  
Author(s):  
Yi Ren ◽  
Panos Y. Papalambros
Keyword(s):  
Preference Elicitation ◽  
Black Box ◽  
User Preference ◽  
Implicit Feedback ◽  
Optimization Approach ◽  
Preference Model ◽  
Binary Choices ◽  
Design Preference ◽  
Query Algorithm ◽  
Query Algorithms

We define preference elicitation as an interaction, consisting of a sequence of computer queries and human implicit feedback (binary choices), from which the user’s most preferred design can be elicited. The difficulty of this problem is that, while a human-computer interaction must be short to be effective, query algorithms usually require lengthy interactions to perform well. We address this problem in two steps. A black-box optimization approach is introduced: The query algorithm retrieves and updates a user preference model during the interaction and creates the next query containing designs that are both likely to be preferred and different from existing ones. Next, a heuristic based on accumulated elicitations from previous users is employed to shorten the current elicitation by querying preferred designs from previous users (the “crowd”) who share similar preferences to the current one.

scholarly journals An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

2019 ◽  
Vol 11 (9) ◽  
pp. 182 ◽  
Author(s):  
Paul Sheridan ◽  
Mikael Onsjö ◽  
Claudia Becerra ◽  
Sergio Jimenez ◽  
George Dueñas
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
System Development ◽  
Cold Start ◽  
Star Trek ◽  
User Preference ◽  
Future Research ◽  
Television Series ◽  
K Nearest Neighbors

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.

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