Novel User Preference Recommender System Based on Twitter Profile Analysis

2020 ◽  
pp. 85-93
Author(s):  
Narasimha Rao Vajjhala ◽  
Sandip Rakshit ◽  
Michael Oshogbunu ◽  
Shafiu Salisu
Keyword(s):  
Recommender System ◽  
Profile Analysis ◽  
User Preference

Aspect-Oriented Recommender Systems

2013 ◽  
pp. 55-72
Author(s):  
Punam Bedi ◽  
Sumit Kr Agarwal
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Recommender System ◽  
User Preference ◽  
Preference Learning ◽  
Decision Making Process ◽  
Design Issues ◽  
Aspect Oriented Programming ◽  
Multi Agent ◽  
Oriented Approach

Recommender systems are widely used intelligent applications which assist users in a decision-making process to choose one item amongst a potentially overwhelming set of alternative products or services. Recommender systems use the opinions of members of a community to help individuals in that community by identifying information most likely to be interesting to them or relevant to their needs. Recommender systems have various core design crosscutting issues such as: user preference learning, security, mobility, visualization, interaction etc that are required to be handled properly in order to implement an efficient, good quality and maintainable recommender system. Implementation of these crosscutting design issues of the recommender systems using conventional agent-oriented approach creates the problem of code scattering and code tangling. An Aspect-Oriented Recommender System is a multi agent system that handles core design issues of the recommender system in a better modular way by using the concepts of aspect oriented programming, which in turn improves the system reusability, maintainability, and removes the scattering and tangling problems from the recommender system.

Interactive Product Catalog for M-Commerce

2007 ◽  
pp. 345-351
Author(s):  
S. Guan ◽  
Y. Tay
Keyword(s):  
Mobile Device ◽  
Recommender System ◽  
Personal Digital Assistant ◽  
Detection Technique ◽  
User Preference ◽  
Interactive Product ◽  
Product Appearance

We propose a product catalog where browsing is directed by an integrated recommender system. The recommender system is to take incremental feedback in return for browsing assistance. Product appearance in the catalog will be dynamically determined at runtime based on user preference detected by the recommender system. The design of our hybrid m-commerce catalog-recommender system investigated the typical constraints of m-commerce applications to conceptualize a suitable catalog interface. The scope was restricted to the case of having a personal digital assistant (PDA) as the mobile device. Thereafter, a preference detection technique was developed to serve as the recommender layer of the system.

Investigation About Applicability of Personal Values for Recommender System

2012 ◽  
Vol 16 (3) ◽  
pp. 404-411 ◽  
Author(s):  
Shunichi Hattori ◽  
◽  
Yasufumi Takama
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Personal Values ◽  
User Preference

A recommender systemis a fundamental technique for finding information that is likely to be preferred by users among vast amounts of information. While existing recommender systems usually employ user preference or attributes of items to make recommendations, marketing fields have been taking notice of personal values, because that such values are significantly related to user preference. This paper investigates the applicability of personal values in modeling items and users. The results of questionnaires show the feasibility of a recommender system based on personal values.

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 Personality or Value: A Comparative Study of Psychographic Segmentation Based on an Online Review Enhanced Recommender System

2019 ◽  
Vol 9 (10) ◽  
pp. 1992 ◽  
Author(s):  
Hui Liu ◽  
Yinghui Huang ◽  
Zichao Wang ◽  
Kai Liu ◽  
Xiangen Hu ◽  
...  
Keyword(s):  
Big Data ◽  
Language Processing ◽  
Recommender System ◽  
Consumer Preferences ◽  
Explanatory Power ◽  
Marketing Research ◽  
User Preferences ◽  
User Preference ◽  
Data Driven ◽  
Product Categories

Big consumer data promises to be a game changer in applied and empirical marketing research. However, investigations of how big data helps inform consumers’ psychological aspects have, thus far, only received scant attention. Psychographics has been shown to be a valuable market segmentation path in understanding consumer preferences. Although in the context of e-commerce, as a component of psychographic segmentation, personality has been proven to be effective for prediction of e-commerce user preferences, it still remains unclear whether psychographic segmentation is practically influential in understanding user preferences across different product categories. To the best of our knowledge, we provide the first quantitative demonstration of the promising effect and relative importance of psychographic segmentation in predicting users’ online purchasing preferences across different product categories in e-commerce by using a data-driven approach. We first construct two online psychographic lexicons that include the Big Five Factor (BFF) personality traits and Schwartz Value Survey (SVS) using natural language processing (NLP) methods that are based on behavior measurements of users’ word use. We then incorporate the lexicons in a deep neural network (DNN)-based recommender system to predict users’ online purchasing preferences considering the new progress in segmentation-based user preference prediction methods. Overall, segmenting consumers into heterogeneous groups surprisingly does not demonstrate a significant improvement in understanding consumer preferences. Psychographic variables (both BFF and SVS) significantly improve the explanatory power of e-consumer preferences, whereas the improvement in prediction power is not significant. The SVS tends to outperform BFF segmentation, except for some product categories. Additionally, the DNN significantly outperforms previous methods. An e-commerce-oriented SVS measurement and segmentation approach that integrates both BFF and the SVS is recommended. The strong empirical evidence provides both practical guidance for e-commerce product development, marketing and recommendations, and a methodological reference for big data-driven marketing research.

scholarly journals A Personalized Music Recommender System Using User Contents, Music Contents and Preference Ratings

2020 ◽  
Vol 07 (01) ◽  
pp. 77-92
Author(s):  
Ja-Hwung Su ◽  
Chu-Yu Chin ◽  
Yi-Wen Liao ◽  
Hsiao-Chuan Yang ◽  
Vincent S. Tseng ◽  
...  
Keyword(s):  
Recommender System ◽  
Mean Squared Error ◽  
Communication Technologies ◽  
User Preference ◽  
Acoustic Features ◽  
Rating Data ◽  
The Arts ◽  
Online Music ◽  
Preference Ratings ◽  
Better Than

Recently, the advances in communication technologies have made music retrieval easier. Without downloading the music, the users can listen to music through online music websites. This incurs a challenging issue of how to provide the users with an effective online listening service. Although a number of past studies paid attention to this issue, the problems of new user, new item and rating sparsity are not easy to solve. To deal with these problems, in this paper, we propose a novel music recommender system that fuses user contents, music contents and preference ratings to enhance the music recommendation. For dealing with problem of new user, the user similarities are calculated by user profiles instead of traditional ratings. By the user similarities, the unknown ratings can be predicted using user-based Collaborative Filtering (CF). For dealing with problems of rating sparsity and new items, the unknown ratings are initialized by acoustic features and music genre ratings. Because the unknown ratings are initially imputed, the rating data will be enriched. Thereupon, the user preference can be predicted effectively by item-based CF. The evaluation results show that our proposed music recommender system performs better than the state-of-the-arts methods in terms of Root Mean Squared Error.

Interactive Personalized Catalogue for M-Commerce

2008 ◽  
pp. 218-228
Author(s):  
Sheng-Uei Guan ◽  
Yuan Sherng Tay
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Recommender System ◽  
User Interfaces ◽  
Personal Digital Assistant ◽  
User Preference ◽  
Screen Size ◽  
Size Constraints ◽  
Small Form Factor ◽  
Small Screen

M-commerce possesses two distinctive characteristics that distinguish it from traditional e-commerce: the mobile setting and the small form factor of mobile devices. Of these, the size of a mobile device will remain largely unchanged due to the tradeoff between size and portability. Small screen size and limited input capabilities pose a great challenge for developers to conceptualize user interfaces that have good usability while working within the size constraints of the device. In response to the limited screen size of mobile devices, there has been unspoken consensus that certain tools must be made available to aid users in coping with the relatively large volume of information. Recommender systems have been proposed to narrow down choices before presenting them to the user (Feldman, 2000). We propose a product catalogue where browsing is directed by an integrated recommender system. The recommender system is to take incremental feedback in return for browsing assistance. Product appearance in the catalogue will be dynamically determined at runtime based on user preference detected by the recommender system. The design of our hybrid m-commerce catalogue recommender system investigated the typical constraints of m-commerce applications to conceptualize a suitable catalogue interface. The scope was restricted to the case of having personal digital assistant (PDA) as the mobile device. Thereafter, a preference detection technique was developed to serve as the recommender layer of the system.

TRACE: Travel Reinforcement Recommendation Based on Location-Aware Context Extraction

2022 ◽  
Vol 16 (4) ◽  
pp. 1-22
Author(s):  
Zhe Fu ◽  
Li Yu ◽  
Xi Niu
Keyword(s):  
Real Time ◽  
Recommender System ◽  
Ad Hoc ◽  
Learning Model ◽  
User Preference ◽  
Context Learning ◽  
User Context ◽  
Location Aware ◽  
Proposed Model ◽  
Online Travel

As the popularity of online travel platforms increases, users tend to make ad-hoc decisions on places to visit rather than preparing the detailed tour plans in advance. Under the situation of timeliness and uncertainty of users’ demand, how to integrate real-time context into dynamic and personalized recommendations have become a key issue in travel recommender system. In this article, by integrating the users’ historical preferences and real-time context, a location-aware recommender system called TRACE ( T ravel R einforcement Recommendations Based on Location- A ware C ontext E xtraction) is proposed. It captures users’ features based on location-aware context learning model, and makes dynamic recommendations based on reinforcement learning. Specifically, this research: (1) designs a travel reinforcing recommender system based on an Actor-Critic framework, which can dynamically track the user preference shifts and optimize the recommender system performance; (2) proposes a location-aware context learning model, which aims at extracting user context from real-time location and then calculating the impacts of nearby attractions on users’ preferences; and (3) conducts both offline and online experiments. Our proposed model achieves the best performance in both of the two experiments, which demonstrates that tracking the users’ preference shifts based on real-time location is valuable for improving the recommendation results.

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.

scholarly journals Restaurant Recommendation in Vehicle Context Based on Prediction of Traffic Conditions

2021 ◽  
Vol 35 (10) ◽  
pp. 2159044
Author(s):  
Zehong Wang ◽  
Jianhua Liu ◽  
Shigen Shen ◽  
Minglu Li
Keyword(s):  
Deep Learning ◽  
Recommender System ◽  
Selection Method ◽  
User Preference ◽  
The Internet ◽  
Mobile Environment ◽  
Internet Of Vehicles ◽  
Second Stage ◽  
Traffic Conditions ◽  
Deep Learning Model

Restaurant recommendation is one of the most recommendation problems because the result of recommendation varies in different environments. Many methods have been proposed to recommend restaurants in a mobile environment by considering user preference, restaurant attributes, and location. However, there are few restaurant recommender systems according to the internet of vehicles environment. This paper presents a recommender system based on the prediction of traffic conditions in the internet of vehicles environment. This recommender system uses a phased selection method to recommend restaurants. The first stage is to screen restaurants that are on the user’s driving route; the second stage is to recommend restaurants from the user attributes, restaurant attributes (with traffic conditions), and vehicle context, using a deep learning model. The experimental evaluation shows that the proposed recommender system is both efficient and effective.

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