On the Importance of Subtext in Recommender Systems

i-com ◽  
2015 ◽  
Vol 14 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Peter Grasch ◽  
Alexander Felfernig
Keyword(s):  
Empirical Study ◽  
Recommender Systems ◽  
Recommender System ◽  
Unsolved Problem ◽  
Preference Elicitation ◽  
User Preferences ◽  
Session Length ◽  
Knowledge Based ◽  
Paralinguistic Information ◽  
Recommendation Strategy

AbstractConversational recommender systems have been shown capable of allowing users to navigate even complex and unknown application domains effectively. However, optimizing preference elicitation remains a largely unsolved problem. In this paper we introduce SPEECHREC, a speech-enabled, knowledge-based recommender system, that engages the user in a natural-language dialog, identifying not only purely factual constraints from the users’ input, but also integrating nuanced lexical qualifiers and paralinguistic information into the recommendation strategy. In order to assess the viability of this concept, we present the results of an empirical study where we compare SPEECHREC to a traditional knowledge-based recommender system and show how incorporating more granular user preferences in the recommendation strategy can increase recommendation quality, while reducing median session length by 46 %.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

Toward Geospatial Collaborative Tourism Recommender Systems

2018 ◽  
pp. 212-248
Author(s):  
Zahra Bahramian ◽  
Rahim Ali Abbaspour ◽  
Christophe Claramunt
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
World Wide ◽  
Information Overload ◽  
Spatial Information ◽  
User Profile ◽  
User Preferences ◽  
Research Issues ◽  
The World ◽  
Information Finding

Tourism activities are highly dependent on spatial information. Finding the most interesting travel destinations and attractions and planning a trip are still open research issues to GIScience research applied to the tourism domain. Nowadays, huge amounts of information are available over the world wide web that may be useful in planning a visit to destinations and attractions. However, it is often time consuming for a user to select the most interesting destinations and attractions and plan a trip according to his own preferences. Tourism recommender systems (TRSs) can be used to overcome this information overload problem and to propose items taking into account the user preferences. This chapter reviews related topics in tourism recommender systems including different tourism recommendation approaches and user profile representation methods applied in the tourism domain. The authors illustrate the potential of tourism recommender systems as applied to the tourism domain by the implementation of an illustrative geospatial collaborative recommender system using the Foursquare dataset.

Task Allocation in Case-Based Recommender Systems

2011 ◽  
pp. 268-279 ◽  
Author(s):  
Fabiana Lorenzi ◽  
Daniela Scherer dos Santos ◽  
Denise de Oliveira ◽  
Ana L.C. Bazzan
Keyword(s):  
Swarm Intelligence ◽  
Recommender Systems ◽  
Recommender System ◽  
Task Allocation ◽  
User Preferences ◽  
Cooperative Agents ◽  
Over Time ◽  
Case Based

Case-based recommender systems can learn about user preferences over time and automatically suggest products that fit these preferences. In this chapter, we present such a system, called CASIS. In CASIS, we combined the use of swarm intelligence in the task allocation among cooperative agents applied to a case-based recommender system to help the user to plan a trip.

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 fuzzy preference tree-based recommender system for medical database

2017 ◽  
Vol 7 (1.1) ◽  
pp. 319
Author(s):  
Pradeepini Gera ◽  
Vishnu Bhargavi Sabbisetty ◽  
Tejaswini Devarasetty ◽  
Madhusri Nukala ◽  
Navyasri Vittamsetty
Keyword(s):  
Recommender Systems ◽  
Fuzzy Set ◽  
Recommender System ◽  
Critical Path ◽  
User Preferences ◽  
Medical Database ◽  
Matching Method ◽  
Tree Structures ◽  
Medical Dataset ◽  
Fuzzy Preference

Nowadays every online site is using personalized recommender systems to suggest a right product for the customer. But existing system has tree structures and have unrequired items in the user preferences. So, it requires high memory and time. To overcome this issue,proposed a new method with increased performance. Firstly, introduced a technique for modeling fuzzy tree-established consumer pref-erences, in which fuzzy set techniques are used to express user choices. A recommendation approach to recommend tree-dependent items is then advanced. The critical path on this study is a comprehensive tree matching method, which can compare two tree-established facts and identify their corresponding components by taking into consideration of all the records on tree structures, weights, and the nodeattributes.The proposed fuzzy preference tree based recommender system is tested using a medical dataset.

PREFERENCE DOMINANCE REASONING FOR CONVERSATIONAL RECOMMENDER SYSTEMS: A COMPARISON BETWEEN A COMPARATIVE PREFERENCES AND A SUM OF WEIGHTS APPROACH

2011 ◽  
Vol 20 (04) ◽  
pp. 591-616 ◽  
Author(s):  
WALID TRABELSI ◽  
NIC WILSON ◽  
DEREK BRIDGE ◽  
FRANCESCO RICCI
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Lexicographic Order ◽  
User Preferences ◽  
Query Suggestion ◽  
Small Set

A conversational recommender system iteratively shows a small set of options for its user to choose between. In order to select these options, the system may analyze the queries tried by the user to derive whether one option is dominated by others with respect to the user's preferences. The system can then suggest that the user try one of the undominated options, as they represent the best options in the light of the user preferences elicited so far. This paper describes a framework for preference dominance. Two instances of the framework are developed for query suggestion in a conversational recommender system. The first instance of the framework is based on a basic quantitative preferences formalism, where options are compared using sums of weights of their features. The second is a qualitative preference formalism, using a language that generalises CP-nets, where models are a kind of generalised lexicographic order. A key feature of both methods is that deductions of preference dominance can be made efficiently, since this procedure needs to be applied for many pairs of options. We show that, by allowing the recommender to focus on undominated options, which are ones that the user is likely to be contemplating, both approaches can dramatically reduce the amount of advice the recommender needs to give to a user compared to what would be given by systems without this kind of reasoning.

scholarly journals Integrating contextual sentiment analysis in collaborative recommender systems

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248695
Author(s):  
Nurul Aida Osman ◽  
Shahrul Azman Mohd Noah ◽  
Mohammad Darwich ◽  
Masnizah Mohd
Keyword(s):  
Sentiment Analysis ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Contextual Information ◽  
User Preferences ◽  
Analysis Technique ◽  
Rating Model ◽  
Multiple Domains ◽  
Filtering Approach

Recently. recommender systems have become a very crucial application in the online market and e-commerce as users are often astounded by choices and preferences and they need help finding what the best they are looking for. Recommender systems have proven to overcome information overload issues in the retrieval of information, but still suffer from persistent problems related to cold-start and data sparsity. On the flip side, sentiment analysis technique has been known in translating text and expressing user preferences. It is often used to help online businesses to observe customers’ feedbacks on their products as well as try to understand customer needs and preferences. However, the current solution for embedding traditional sentiment analysis in recommender solutions seems to have limitations when involving multiple domains. Therefore, an issue called domain sensitivity should be addressed. In this paper, a sentiment-based model with contextual information for recommender system was proposed. A novel solution for domain sensitivity was proposed by applying a contextual information sentiment-based model for recommender systems. In evaluating the contributions of contextual information in sentiment-based recommendations, experiments were divided into standard rating model, standard sentiment model and contextual information model. Results showed that the proposed contextual information sentiment-based model illustrates better performance as compared to the traditional collaborative filtering approach.

scholarly journals AKNOBAS: A knowledge-based segmentation recommender system based on intelligent data mining techniques

2012 ◽  
Vol 9 (2) ◽  
pp. 713-740 ◽  
Author(s):  
Alejandro Rodríguez-González ◽  
Javier Torres-Niño ◽  
Enrique Jimenez-Domingo ◽  
Miguel Gomez-Berbis ◽  
Giner Alor-Hernandez
Keyword(s):  
Data Mining ◽  
Competitive Advantage ◽  
Recommender Systems ◽  
Business Model ◽  
Recommender System ◽  
Added Value ◽  
Proof Of Concept ◽  
Clustering Techniques ◽  
Knowledge Based ◽  
Substantial Progress

Recommender Systems have recently undergone an unwavering improvement in terms of efficiency and pervasiveness. They have become a source of competitive advantage in many companies which thrive on them as the technological core of their business model. In recent years, we have made substantial progress in those Recommender Systems outperforming the accuracy and added-value of their predecessors, by using cutting-edge techniques such as Data Mining and Segmentation. In this paper, we present AKNOBAS, a Knowledge-based Segmentation Recommender System, which follows that trend using Intelligent Clustering Techniques for Information Systems. The contribution of this Recommender System has been validated through a business scenario implementation proof-of-concept and provides a clear breakthrough of marshaling information through AI techniques.

scholarly journals Multi-stakeholder News Recommendation Using Hypergraph Learning

2020 ◽  
pp. 531-535 ◽  
Author(s):  
Alireza Gharahighehi ◽  
Celine Vens ◽  
Konstantinos Pliakos
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
User Preferences ◽  
Multiple Stakeholders ◽  
Hypergraph Learning ◽  
News Agencies ◽  
Ranking Task ◽  
Multi Stakeholder ◽  
News Recommendation ◽  
Ranked List

AbstractRecommender systems are meant to fulfil user preferences. Nevertheless, there are multiple examples where users are not the only stakeholder in a recommendation platform. For instance, in news aggregator websites apart from readers, one can consider magazines (news agencies) or authors as other stakeholders. A multi-stakeholder recommender system generates a ranked list of items taking into account the preferences of multiple stakeholders. In this study, news recommendation is handled as a hypergraph ranking task, where relations between multiple types of objects and stakeholders are modeled in a unified hypergraph. The obtained results indicate that ranking on hypergraphs can be utilized as a natural multi-stakeholder recommender system that is able to adapt recommendations based on the importance of stakeholders.

A social recommender system by combining social network and sentiment similarity: A case study of healthcare

2016 ◽  
Vol 43 (5) ◽  
pp. 635-648 ◽  
Author(s):  
Donghui Yang ◽  
Chao Huang ◽  
Mingyang Wang
Keyword(s):  
Social Network ◽  
Social Context ◽  
Recommender Systems ◽  
Network Structure ◽  
Recommender System ◽  
User Preferences ◽  
Social Recommender System ◽  
Social Recommender Systems ◽  
The Social ◽  
New Framework

Social recommender systems aim to support user preferences and help users make better decisions in social media. The social network and the social context are two vital elements in social recommender systems. In this contribution, we propose a new framework for a social recommender system based on both network structure analysis and social context mining. Exponential random graph models (ERGMs) are able to capture and simulate the complex structure of a micro-blog network. We derive the prediction formula from ERGMs for recommending micro-blog users. Then, a primary recommendation list is created by analysing the micro-blog network structure. In the next step, we calculate the sentiment similarities of micro-blog users based on a sentiment feature set which is extracted from users’ tweets. Sentiment similarities are used to filter the primary recommendation list and find users who have similar attitudes on the same topic. The goal of those two steps is to make the social recommender system much more precise and to satisfy users’ psychological preferences. At the end, we use this new framework deal with big real-world data. The recommendation results of diabetes accounts of Weibo show that our method outperforms other social recommender systems.

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