Personalization Approaches for Ranking

2017 ◽  
Vol 7 (1) ◽  
pp. 1-16
Author(s):  
Madhuri A. Potey ◽  
Pradeep K. Sinha
Keyword(s):  
User Satisfaction ◽  
State Of The Art ◽  
User Model ◽  
Research Trends ◽  
User Preferences ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Information Need ◽  
And Behavior ◽  
Available Information

Search engine technologies are evolving to satisfy the user's ever increasing information need; but are yet to achieve perfection especially in ranking. With the exponential growth in the available information on the internet; ranking has become vital for satisfactory search experience. User satisfaction can be ensured to some extent by personalizing the search results based on user preferences which can be explicitly stated or learned from user's search behavior. Machine learning algorithms which predict user preference from the available information related to the user are extensively experimented for personalization. Among several studies undertaken for re-ranking the documents, many focus on the user. Such approaches create user model to capture the search context and behavior. This study attempts to analyze the research trends in user model based personalization and discuss experimental results in personalized information retrieval area. The authors experimented to extend the state of the art in the specific areas of personalization.

scholarly journals Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 344
Author(s):  
Alejandro Humberto García Ruiz ◽  
Salvador Ibarra Martínez ◽  
José Antonio Castán Rocha ◽  
Jesús David Terán Villanueva ◽  
Julio Laria Menchaca ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Energy Saving ◽  
User Satisfaction ◽  
Air Conditioning ◽  
State Of The Art ◽  
User Preferences ◽  
The State ◽  
Nsga Ii ◽  
Air Conditioning System

Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease electrical energy consumption. To assess the experimentation, we constructed a simulator by training a backpropagation neural network with real data from a laboratory’s air conditioning system. According to the results, we conclude that NSGA II provides better results than the state of the art (GA) regarding user preferences and energy-saving.

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 Learning Personalized End-to-End Goal-Oriented Dialog

2019 ◽  
Vol 33 ◽  
pp. 6794-6801 ◽  
Author(s):  
Liangchen Luo ◽  
Wenhao Huang ◽  
Qi Zeng ◽  
Zaiqing Nie ◽  
Xu Sun
Keyword(s):  
User Satisfaction ◽  
State Of The Art ◽  
User Preferences ◽  
Task Completion ◽  
Dialog Systems ◽  
Performance Improvements ◽  
Human Evaluation ◽  
Proposed Model ◽  
End To End ◽  
Qualitative Performance

Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues. In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs. We first introduce a PROFILE MODEL which encodes user profiles into distributed embeddings and refers to conversation history from other similar users. Then a PREFERENCE MODEL captures user preferences over knowledge base entities to handle the ambiguity in user requests. The two models are combined into the PERSONALIZED MEMN2N. Experiments show that the proposed model achieves qualitative performance improvements over state-of-the-art methods. As for human evaluation, it also outperforms other approaches in terms of task completion rate and user satisfaction.

scholarly journals Machine-Learning-Based User Position Prediction and Behavior Analysis for Location Services

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 180
Author(s):  
Haiyang Jiang ◽  
Mingshu He ◽  
Yuanyuan Xi ◽  
Jianqiu Zeng
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Shopping Mall ◽  
Training Dataset ◽  
Shopping Malls ◽  
Location Services ◽  
Proposed Model ◽  
And Behavior ◽  
Speed Accuracy

Machine learning (ML)-based methods are increasingly used in different fields of business to improve the quality and efficiency of services. The increasing amount of data and the development of artificial intelligence algorithms have improved the services provided to customers in shopping malls. Most new services are based on customers’ precise positioning in shopping malls, especially customer positioning within shops. We propose a novel method to accurately predict the specific shops in which customers are located in shopping malls. We use global positioning system (GPS) information provided by customers’ mobile terminals and WiFi information that completely covers the shopping mall. According to the prediction results, we learn some of the behavior preferences of users. We use these predicted customer locations to provide customers with more accurate services. Our training dataset is built using feature extraction and screening from some real customers’ transaction records in shopping malls. In order to prove the validity of the model, we also cross-check our algorithm with a variety of machine learning algorithms. Our method achieves the best speed–accuracy trade-off and can accurately locate the shops in which customers are located in shopping malls in real time. Compared to other algorithms, the proposed model is more accurate. User preference behaviors can be used in applications to efficiently provide more tailored services.

Cross Domain Framework for Implementing Recommendation Systems Based on Context Based Implicit Negative Feedback

2012 ◽  
Vol 3 (1) ◽  
pp. 22-36
Author(s):  
Maitri Jhaveri ◽  
Jyoti Pareek
Keyword(s):  
Negative Feedback ◽  
Information Overload ◽  
Recommendation Systems ◽  
User Preferences ◽  
User Preference ◽  
P2p Networks ◽  
Cross Domain ◽  
Collaborative Environment ◽  
Available Information ◽  
Novel Model

The last decade met a remarkable proliferation of P2P networks, PDMS, semantic web, communitarian websites, electronic stores, etc. resulting in an overload of available information. One of the solutions to this information overload problem is using efficient tools such as the recommender system which is a personalization system that helps users to find items of interest based on their preferences. Several such recommendation engines do exist under different domains. However these recommendation systems are not very effective due to several issues like lack of data, changing data, changing user preferences, and unpredictable items. This paper proposes a novel model of Recommendation systems in e-commerce domain which will address issues of cold start problem and change in user preference problem. This model is based on studying implicit negative feedback from users in cross domain collaborative environment to identify user preferences effectively. The authors have also identified a list of parameters for this study.

Design and Manufacturing of 3D Printed Foods With User Validation

2020 ◽  
Author(s):  
Stefania Chirico Scheele ◽  
Martin Binks ◽  
Paul F. Egan
Keyword(s):  
Additive Manufacturing ◽  
User Satisfaction ◽  
Three Dimensional ◽  
User Preferences ◽  
User Preference ◽  
Great Promise ◽  
High Fidelity ◽  
Standard Design ◽  
3D Printed ◽  
Overhang Angle

Abstract Additive manufacturing is becoming widely practical for diverse engineering applications, with emerging approaches showing great promise in the food industry. From the realization of complex food designs to the automated preparation of personalized meals, 3D printing promises many innovations in the food manufacturing sector. However, its use is limited due to the need to better understand manufacturing capabilities for different food materials and user preferences for 3D food prints. Our study aims to explore the 3D food printability of design features, such as overhangs and holes, and assess how well they print through quantitative and qualitative measurements. Designs with varied angles and diameters based on the standard design limitations for additive manufacturing were printed and measured using marzipan and chocolate. It was found that marzipan material has a minimum feature size for overhang design at 55° and for hole design at 4mm, while chocolate material has a minimum overhang angle size of 35° and does not reliably print holes. Users were presented a series of designs to determine user preference (N = 30) towards the importance of fidelity and accuracy between the expected design and the 3D printed sample, and how much they liked each sample. Results suggest that users prefer designs with high fidelity to their original shape and perceive the current accuracy/precision of 3D printers sufficient for accurately printing three-dimensional geometries. These results demonstrate the current manufacturing capabilities for 3D food printing and success in achieving high fidelity designs for user satisfaction. Both of these considerations are essential steps in providing automated and personalized manufacturing for specific user needs and preferences.

scholarly journals Adapting a Virtual Advisor’s Verbal Conversation Based on Predicted User Preferences: A Study of Neutral, Empathic and Tailored Dialogue

2020 ◽  
Vol 4 (3) ◽  
pp. 55
Author(s):  
Hedieh Ranjbartabar ◽  
Deborah Richards ◽  
Ayse Aysin Bilgin ◽  
Cat Kutay ◽  
Samuel Mascarenhas
Keyword(s):  
Deep Understanding ◽  
User Model ◽  
User Preferences ◽  
Psychological State ◽  
Virtual Agents ◽  
Agent Architecture ◽  
Neutral Group ◽  
Gender Culture ◽  
Current State ◽  
And Behavior

Virtual agents that improve the lives of humans need to be more than user-aware and adaptive to the user’s current state and behavior. Additionally, they need to apply expertise gained from experience that drives their adaptive behavior based on deep understanding of the user’s features (such as gender, culture, personality, and psychological state). Our work has involved extension of FAtiMA (Fearnot AffecTive Mind Architecture) with the addition of an Adaptive Engine to the FAtiMA cognitive agent architecture. We use machine learning to acquire the agent’s expertise by capturing a collection of user profiles into a user model and development of agent expertise based on the user model. In this paper, we describe a study to evaluate the Adaptive Engine, which compares the benefit (i.e., reduced stress, increased rapport) of tailoring dialogue to the specific user (Adaptive group) with dialogues that are either empathic (Empathic group) or neutral (Neutral group). Results showed a significant reduction in stress in the empathic and neutral groups, but not the adaptive group. Analyses of rule accuracy, participants’ dialogue preferences, and individual differences reveal that the three groups had different needs for empathic dialogue and highlight the importance and challenges of getting the tailoring right.

scholarly journals Modeling user preference dynamics with coupled tensor factorization for social media recommendation

2020 ◽  
Author(s):  
Hamidreza Tahmasbi ◽  
Mehrdad Jalali ◽  
Hassan Shakeri
Keyword(s):  
Social Media ◽  
User Satisfaction ◽  
User Preferences ◽  
User Preference ◽  
Tensor Factorization ◽  
Plausible Assumption ◽  
The Past ◽  
Proposed Model ◽  
Recommendation Accuracy ◽  
Over Time

AbstractAn essential problem in real-world recommender systems is that user preferences are not static and users are likely to change their preferences over time. Recent studies have shown that the modelling and capturing the dynamics of user preferences lead to significant improvements on recommendation accuracy and, consequently, user satisfaction. In this paper, we develop a framework to capture user preference dynamics in a personalized manner based on the fact that changes in user preferences can vary individually. We also consider the plausible assumption that older user activities should have less influence on a user’s current preferences. We introduce an individual time decay factor for each user according to the rate of his preference dynamics to weigh the past user preferences and decrease their importance gradually. We exploit users’ demographics as well as the extracted similarities among users over time, aiming to enhance the prior knowledge about user preference dynamics, in addition to the past weighted user preferences in a developed coupled tensor factorization technique to provide top-K recommendations. The experimental results on the two real social media datasets—Last.fm and Movielens—indicate that our proposed model is better and more robust than other competitive methods in terms of recommendation accuracy and is more capable of coping with problems such as cold-start and data sparsity.

scholarly journals A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

2021 ◽  
Vol 11 (8) ◽  
pp. 3719
Author(s):  
Sun-Young Ihm ◽  
Shin-Eun Lee ◽  
Young-Ho Park ◽  
Aziz Nasridinov ◽  
Miyeon Kim ◽  
...  
Keyword(s):  
Missing Data ◽  
Collaborative Filtering ◽  
State Of The Art ◽  
High Reliability ◽  
User Preferences ◽  
User Preference ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Common Interests ◽  
Recommendation Accuracy

Collaborative filtering (CF) is a recommendation technique that analyzes the behavior of various users and recommends the items preferred by users with similar preferences. However, CF methods suffer from poor recommendation accuracy when the user preference data used in the recommendation process is sparse. Data imputation can alleviate the data sparsity problem by substituting a virtual part of the missing user preferences. In this paper, we propose a k-recursive reliability-based imputation (k-RRI) that first selects data with high reliability and then recursively imputes data with additional selection while gradually lowering the reliability criterion. We also propose a new similarity measure that weights common interests and indifferences between users and items. The proposed method can overcome disregarding the importance of missing data and resolve the problem of poor data imputation of existing methods. The experimental results demonstrate that the proposed approach significantly improves recommendation accuracy compared to those resulting from the state-of-the-art methods while demanding less computational complexity.

scholarly journals Female-Type Android’s Drive to Quickly Understand a User’s Concept of Preferences Stimulates Dialogue Satisfaction: Dialogue Strategies for Modeling User’s Concept of Preferences

2021 ◽  
Author(s):  
Takahisa Uchida ◽  
Takashi Minato ◽  
Yutaka Nakamura ◽  
Yuichiro Yoshikawa ◽  
Hiroshi Ishiguro
Keyword(s):  
User Satisfaction ◽  
Design Theory ◽  
User Preferences ◽  
Willingness To Communicate ◽  
User Preference ◽  
Female Type ◽  
Task Oriented ◽  
Android Robot ◽  
User Motivation

AbstractThis research develops a conversational robot that stimulates users’ dialogue satisfaction and motivation in non-task-oriented dialogues that include opinion and/or preference exchanges. One way to improve user satisfaction and motivation is by demonstrating the robot’s ability to understand user opinions. In this paper, we explore a method that efficiently obtains the concept of user preferences: likes and dislikes. The concept is acquired by complementing a small amount of user preference data observed in dialogues. As a method for efficient collection, we propose a dialogue strategy that creates utterances with the largest expected complementation. Our experimental results with a female-type android robot suggest that the proposed strategy efficiently obtained user preferences and enhanced dialogue satisfaction. In addition, the strength of user motivation (i.e., long-term willingness to communicate with the android) is only positively correlated with the android’s willingness to understand. Our results not only show the effectiveness of our proposed strategy but also suggest a design theory for dialogue robots to stimulate dialogue motivation, although the current results are derived only from a female-type android.

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