User Preference Model Research Articles

A group preference-based item similarity model: comparison of clustering techniques in ambient and context-aware recommender systems

In the study of collaborative filtering, scholars and professionals alike have given much attention to user responses of the “one-class” type, feedback like online transactions or “likes”. Such behavior gauges have been integral to many ambient intelligent and context-aware recommendation systems, in which users are furnished with personalized lists of items according to their exhibited tastes. These one-class data, earlier studies have shown, are readily grasped by Bayesian personalized ranking, a pairwise preference assumption. Nevertheless, these works fail to make sufficient use of item similarity models using group preference. To improve performance, we argue in this paper, it is necessary to develop a model that yokes a User preference model to the Group Preference-based Similarity models (called UGPS). UCPG will produce a greater depth of interactions, we argue, because it takes on an entire set of items as opposed to the solitary item used previously. Moreover, a number of clustering methods have been put to work in group preference-based recommendation systems, but there is no consensus as to which offers superior accuracy. To gain clarity, we first built up a pair of versions of UGPS in order to assess the recommendation performances of different approaches to group generation: UGPS-1, which employed K-means, and UGPS-2, using K-NN—according to how efficiently they group their output. This comparison revealed that UGPS-1 tended to improve its recommendation performance as the number of groups and representative item sets grew. In contrast, UGPS-2 exhibited the opposite effect: recommendation performance declined as the number of groups and representative item sets expanded. Lastly, we consider how our UGPS system works with various sophisticated approaches on four real datasets, and demonstrate that UGPS produces more accurate recommendations.

A Cognitively Inspired Clustering Approach for Critique-Based Recommenders

The purpose of recommender systems is to support humans in the purchasing decision-making process. Decision-making is a human activity based on cognitive information. In the field of recommender systems, critiquing has been widely applied as an effective approach for obtaining users’ feedback on recommended products. In the last decade, there have been a large number of proposals in the field of critique-based recommenders. These proposals mainly differ in two aspects: in the source of data and in how it is mined to provide the user with recommendations. To date, no approach has mined data using an adaptive clustering algorithm to increase the recommender’s performance. In this paper, we describe how we added a clustering process to a critique-based recommender, thereby adapting the recommendation process and how we defined a cognitive user preference model based on the preferences (i.e., defined by critiques) received by the user. We have developed several proposals based on clustering, whose acronyms are MCP, CUM, CUM-I, and HGR-CUM-I. We compare our proposals with two well-known state-of-the-art approaches: incremental critiquing (IC) and history-guided recommendation (HGR). The results of our experiments showed that using clustering in a critique-based recommender leads to an improvement in their recommendation efficiency, since all the proposals outperform the baseline IC algorithm. Moreover, the performance of the best proposal, HGR-CUM-I, is significantly superior to both the IC and HGR algorithms. Our results indicate that introducing clustering into the critique-based recommender is an appealing option since it enhances overall efficiency, especially with a large data set.

End-to-end task dependent recurrent entity network for goal-oriented dialog learning

In this paper, we introduce the Task Dependent Recurrent Entity Network (TDREN) to solve Dialogue System Technology Challenges 6 (DSTC 6) track 1. Traditionally, there have been methods such as collecting the intent of the user in a conversation directly using rules. We design an end-to-end structure that properly models the restaurant pre-related user preferences that appear in the dialogue and gives appropriate responses. We perform experiments on the TDREN and achieved 97.7% at precision 1. We propose a new artificial neural network structure and recurrent cell for modeling user preference information. Then, we show that task-oriented dialogue modeling experiment results using the structure and the recurrent cell.

Personalized Recommendation Algorithm Based on Product Reviews

Under the background of leap-forward development for the internet, e-commerce has played an important role in people's daily life, but huge data sizes have also brought problems, such as information overload which can be solved by using a recommendation system effectively. However, with the development of the e-commerce, the amount of the product catalogs and users becomes larger, which causes lower performance of the traditional recommendation system. This article comes up with a personalized recommendation algorithm based on the data mining of product reviews to optimize the performance of the new recommendation system. Features of the product were extracted, for which the users' sentiment polarity was analyzed. This article develops a recommendation system based on the user's preference model and the product features to get the recommendation result. Experimental results show that a personalized recommendation has significantly improved the accuracy and recall rate when compared with a traditional recommendation algorithm.

Robots that maintain equilibrium: Proactivity by reasoning about user intentions and preferences

Robots need to exhibit proactive behavior if they are to be accepted in human-centered environments. A proactive robot must reason about the actions it can perform, the state of the environment, the state and the intentions of its users, and what the users deem desirable. This paper proposes a computational framework for proactive robot behavior that formalizes the above ingredients. The framework is grounded on the notion of Equilibrium Maintenance: current and future states are continuously evaluated to identify opportunities for acting that steer the system into more desirable states. We show that this process leads a robot to proactively generate its own goals and enact them, and that the obtained behavior depends on a model of user intentions, preferences, and the temporal horizon used in prediction. A number of examples show that our framework accounts for even slight variations in user preference models and perceived user intentions. We also show how the level of informedness of the system is easily customizable.

Efficient Mining and Recommendation of Extensive Data Through Collaborative Filtering in E-Commerce: A Survey

E-Commerce is the most widely used technique nowadays. Buying and selling goods on the Internet has been most admired and frequently utilized. The humongous growth of the content available on the internet has made laborious for users to search and utilize information for classifying the products. Recommendation system regarded as the best way to help the customers in buying the related products. (GRS) group recommender system aims at enhancing the customer’s benefits for buying the products. This paper summarizes the fuzzy tree matching, modeling user preference dynamics, web page recommendation, uncertainty analysis for keywords, recommender system application, temporal topic model for friend recommendation, autocratic decision-making system based on (GRS),modeling user recommender, evaluating recommender system and enhancing (GRS).

Probabilistic Inference Over Repeated Insertion Models

Distributions over rankings are used to model user preferences in various settings including political elections and electronic commerce. The Repeated Insertion Model (RIM) gives rise to various known probability distributions over rankings, in particular to the popular Mallows model. However, probabilistic inference on RIM is computationally challenging, and provably intractable in the general case. In this paper we propose an algorithm for computing the marginal probability of an arbitrary partially ordered set over RIM. We analyze the complexity of the algorithm in terms of properties of the model and the partial order, captured by a novel measure termed the "cover width." We also conduct an experimental study of the algorithm over serial and parallelized implementations. Building upon the relationship between inference with rank distributions and counting linear extensions, we investigate the inference problem when restricted to partial orders that lend themselves to efficient counting of their linear extensions.

Normal Cloud Model-Based Algorithm for Multi-Attribute Trusted Cloud Service Selection

With the wide deployment of cloud computing, many security challenges have arisen, such as data and storage integrity and virtualization security. The crisis of trust caused by these security issues has become one of the important factors restricting the wide applications of cloud service. Especially for security-sensitive users, it is challenging to quickly select a cloud service which has the high level of trust and can meet both the user preferences and specific functional demands. This paper explores the multi-granularity selection standard of trust level, the users’ preference calculation model, and the cloud service selection algorithm. First, the trust evaluation mechanisms among different entities in the human society are fitted, and the multi-granularity selection standard of trust levels based on Gaussian cloud transformation is constructed. Then, the calculation model of user preferences based on the cloud analytic hierarchy process is developed. Finally, the trusted cloud service selection algorithm based on two-step fuzzy comprehensive evaluation is proposed and experimentally validated.

Wide and Deep Model of Multi-Source Information-Aware Recommender System

Collaborative filtering recommendation suffers from the problems of high data sparsity, poor expansibility, cold start, and the difficulty of modeling user preferences, among which data sparsity is the greatest issue. Although our previous work on matrix completion model, named low rank non-negative matrix factorization and completion algorithm (LR-NMFC) and stochastic sub-gradient based low rank matrix completion algorithm, could effectively alleviate the sparsity problem, they customarily model the linear feature interactions instead of the complex nonlinear structures between users and items when making recommendations. To better depict user preferences and item features, we deepen the linear model LR-NMFC to establish a wide and deep model, which we named Wide and Deep model of Multi-source information-Aware recommender system (WDMMA), based on multi-source information composed of user-item interaction matrix, attributes, and context. The wide part mainly handles the linear interactions between users and items, while the deep part portrays the high-order nonlinear interactions. We pre-train both the wide and the deep part using LR-NMFC in the embedding layer. In the pooling layer, we define a pooling operation, AC-pooling, which is used to model the various interactions among users, items, attributes, and context information. Upon the pooling layer, we stack some hidden layers to capture the high-order nonlinear feature interactions. Experiments on two public datasets show that WDMMA can learn complex nonlinear feature patterns successfully and effectively and is beneficial to improve the recommendation performance. Therefore, it is an effective way to consider both linear user-item interactions and multi-source information-aware nonlinear interactions in a deep learning framework when making recommendations.

Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation

Point-of-interest (POI) recommendation has become an important way to help people discover attractive and interesting places, especially when they travel out of town. However, the extreme sparsity of user-POI matrix and cold-start issues severely hinder the performance of collaborative filtering-based methods. Moreover, user preferences may vary dramatically with respect to the geographical regions due to different urban compositions and cultures. To address these challenges, we stand on recent advances in deep learning and propose a Spatial-Aware Hierarchical Collaborative Deep Learning model (SH-CDL). The model jointly performs deep representation learning for POIs from heterogeneous features and hierarchically additive representation learning for spatial-aware personal preferences. To combat data sparsity in spatial-aware user preference modeling, both the collective preferences of the public in a given target region and the personal preferences of the user in adjacent regions are exploited in the form of social regularization and spatial smoothing. To deal with the multimodal heterogeneous features of the POIs, we introduce a late feature fusion strategy into our SH-CDL model. The extensive experimental analysis shows that our proposed model outperforms the state-of-the-art recommendation models, especially in out-of-town and cold-start recommendation scenarios.

On route planning by inferring visiting time, modeling user preferences, and mining representative trip patterns

Location-based services allow users to perform check-in actions, which record the geo-spatial activities and provide a plentiful source to do more accurate and useful geographical recommendation. In this paper, we present a novel Preferred Time-aware Route Planning (PTRP) problem, which aims to recommend routes whose locations are not only representative but also need to satisfy users’ preference. The central idea is that the goodness of visiting locations along a route is significantly affected by the visiting time and user preference, and each location has its own proper visiting time due to its category and population. We develop a four-stage preference-based time-aware route planning framework. First, since there is usually either noise time on existing locations or no visiting information on new locations, we devise an inference method, LocTimeInf, to predict the location visiting time on routes. Second, considering the geographical, social, and temporal information of users, we propose the GST-Clus method to group users with similar location visiting preferences. Third, we find the representative and popular time-aware location-transition behaviors by proposing Time-aware Transit Pattern Mining (TTPM) algorithm. Finally, based on the mined time-aware transit patterns, we develop a Preferred Route Search (PR-Search) algorithm to construct the final time-aware routes. Experiments on Gowalla and Foursquare check-in data exhibit the promising effectiveness and efficiency of the proposed methods, comparing to a series of competitors.

AN INFORMATION SERVICE MODEL FOR REMOTE SENSING EMERGENCY SERVICES

Abstract. This paper presents a method on the semantic access environment, which can solve the problem about how to identify the correct natural disaster emergency knowledge and return to the demanders. The study data is natural disaster knowledge text set. Firstly, based on the remote sensing emergency knowledge database, we utilize the sematic network to extract the key words in the input documents dataset. Then, using the semantic analysis based on words segmentation and PLSA, to establish the sematic access environment to identify the requirement of users and match the emergency knowledge in the database. Finally, the user preference model was established, which could help the system to return the corresponding information to the different users. The results indicate that semantic analysis can dispose the natural disaster knowledge effectively, which will realize diversified information service, enhance the precision of information retrieval and satisfy the requirement of users.

An Efficient p2p Content Retrieval in Guided Distributed Environment using Dynamic Search Algorithm

Background/Objectives: The Peer-to-Peer (P2P) network connection can build loosely coupled application that covers effective resource sharing. The main aim of this propounded approach is to develop the search and recoveryoperation in the unstructured p2p networks. Methods/Statistical Analysis: The network can be classified either as structured or unstructured. Since, the p2p networks which are mainly unstructured can be built efficaciously and with more precision, even in severe conditions over the network topology;so as a result, these networks are far more suitable to the network environment. The random search techniques adopted by these types of networks commonly perform less effective in large size network. The search and retrieve performance can be improved in unstructured p2p networks by utilizing users common Preference pattern, which will be captured inside a probability-theoretic structure is called User Preference Model (UPM). It is developed with the support of Dynamic Query Retrieval Protocol. Findings: In our propounded approach, whenever the same file is searched for over three times, the details, present in the routing table will be automatically moving to the User Preference Model Table, hence rendering our propounded searching methodology much faster and efficacious in nature. Improvements/Applications: The application of the research is to be used in the small world unstructured peer to peer networks. The search performance and retrieval performance can be improved in this research. Novelty: In this specific paper, we have propounded a search protocol as well as a routing table updating protocol so as to develop or improve the searching process through self-managing the p2p network structure into a small business world application. Keywords: Dynamic Query Retrieval Protocol, Unstructured Peer to Peer Network, User Preference Model

Document Language Models, Query Models, and Risk Minimization for Information Retrieval

We present a framework for information retrieval that combines document models and query models using a probabilistic ranking function based on Bayesian decision theory. The framework suggests an operational retrieval model that extends recent developments in the language modeling approach to information retrieval. A language model for each document is estimated, as well as a language model for each query, and the retrieval problem is cast in terms of risk minimization. The query language model can be exploited to model user preferences, the context of a query, synonomy and word senses. While recent work has incorporated word translation models for this purpose, we introduce a new method using Markov chains defined on a set of documents to estimate the query models. The Markov chain method has connections to algorithms from link analysis and social networks. The new approach is evaluated on TREC collections and compared to the basic language modeling approach and vector space models together with query expansion using Rocchio. Significant improvements are obtained over standard query expansion methods for strong baseline TF-IDF systems, with the greatest improvements attained for short queries on Web data.

FeRe: Exploiting influence of multi-dimensional features resided in news domain for recommendation

As a promising direction, personalized news recommendation plays an important role in helping readers find interesting news from a gigantic amount of news items. Diverse methods related to news recommendation have been proposed to provide readers with personalized suggestions. However, little research focuses on exploiting influence of domain-specific features resided in news domain for recommendation. The objective of this paper is to build a hybrid news recommendation model, which improves news recommendation performance and alleviates novelly defined sparsity problem through exploiting influence of multi-dimensional domain-specific features from news, and saves much time in finding interesting stories for readers. First of all, we use a domain-specific feature from news, i.e., trends (different news categories have different lifecycle and play different roles in acquiring user profiles), to model user preference. The proposed user preference model dynamically evolves over time, through which, a superior content-based algorithm is proposed. Then, we also employ a domain-specific feature called user sequence with temporal feature, where multi-dimensional factors including user sequence, behavior weight, and time are used to compute similarity between users. The proposed similarity computation strategy is independent of common behaviors such as co-clicking/co-reading. Therefore, based on the similarity, improved collaborative filtering algorithm alleviates sparsity problem to some extent in terms of novelly definted sparsity metric relevant to news data. Besides, we also incorporate general news features into content-based and collaborative filtering news recommendation. Thus, we make the best of multi-dimensional features resided in news for improving recommendation performance. To further improve effectiveness, we investigate the feasibility of combining above two methods into a hybrid model called Fere by employing a no-argument union strategy. We conduct experiments to evaluate the effectiveness and efficiency of Fere by utilizing real news dataset. Experimental results show that Fere outperforms individual methods and baseline hybrid approaches.

Graph‐based recommendation integrating rating history and domain knowledge: Application to on‐site guidance of museum visitors

Visitors to museums and other cultural heritage sites encounter a wealth of exhibits in a variety of subject areas, but can explore only a small number of them. Moreover, there typically exists rich complementary information that can be delivered to the visitor about exhibits of interest, but only a fraction of this information can be consumed during the limited time of the visit. Recommender systems may help visitors to cope with this information overload. Ideally, the recommender system of choice should model user preferences, as well as background knowledge about the museum's environment, considering aspects of physical and thematic relevancy. We propose a personalized graph‐based recommender framework, representing rating history and background multi‐facet information jointly as a relational graph. A random walk measure is applied to rank available complementary multimedia presentations by their relevancy to a visitor's profile, integrating the various dimensions. We report the results of experiments conducted using authentic data collected at the Hecht museum. An evaluation of multiple graph variants, compared with several popular and state‐of‐the‐art recommendation methods, indicates on advantages of the graph‐based approach.

IMPROVING ACCURACY OF MULTI-CRITERIA COLLABORATIVE FILTERING BY NORMALIZING USER RATINGS

Multi-criteria collaborative filtering schemes allow modeling user preferences in a more detailed manner by collecting ratings on various aspects of a product or service. Although preferences are expressed by numerical ratings within a predetermined scale, it is not guaranteed that users comprehend such scale identically. As a result, profiles of users with similar tastes might turn out to be unrelated. Besides, distinct criteria might have different rating scales creating an essential incompatibility with the rating schemes of users which in turn conceals proper relation between main criterion and sub-criteria. Since users rate items based on their personal rating habits, it is essential to determine user similarities according to their rating patterns by normalizing ratings to an identical scale. In this paper, two different normalization methods are studied, i.e., z -score normalization and decoupling normalization, in order to improve accuracy of multi-criteria collaborative filtering systems. In particular, two normalization methods are employed by modifying the state-of-the-art memory-based multi-criteria recommender schemes so that similarities among users are calculated based on preference models rather than pure numerical ratings. Real world data-based experimental results show that both methods, especially decoupling normalization method, provide significant improvements on accuracy of estimated multi-criteria predictions and outperform previous pure numerical ratings-based approach.

A temporal-aware POI recommendation system using context-aware tensor decomposition and weighted HITS

The popularity of location-based social networks (LBSN) provides us with a new perspective for understanding people's travel behaviours and enables a lot of location-based services, such as point of interest (POI) recommendation. However, personalized POI recommendation is very challenging, as the user-location matrix is very sparse for traditional collaborative filtering (CF)-based POI recommendation approaches. The problem becomes even more challenging when people travel to a new city. In addition, temporal influence plays an important role in POI recommendation, for most users tend to visit different kinds of POIs at different time in a day, e.g., visiting a food-related POI at noon and visiting a nightlife spot at night. To the end, we propose a novel POI recommendation system, which consists of two components: context-aware tensor decomposition (CTD) for user preferences modelling and weighted HITS (Hypertext Induced Topic Search)-based POI rating (WHBPR). We model user preferences with a three-dimension tensor (user-category-time). Supplementing the missing entries of the tensor through CTD with the aid of other three matrices, we recover user preferences of different time slots. WHBPR incorporates the impacts of user preferences and social opinions on POI rating. We evaluated our method using the real Foursquare datasets, verifying the advantages of our method beyond other baselines.

Inside the Mind: Using Neuroimaging to Understand Moral Product Preference Judgments Involving Sustainability

Trying to decide whether to purchase a sustainable product often puts decision makers in a difficult situation, especially if the more sustainable option provides less desirable features or costs a premium. This paper theorizes that adding sustainability as a variable during product choice evaluations create decisions that are moral choice scenarios, where benefit to society is weighed against personal gain. From an engineering design perspective, modeling user preferences in this context can be extremely difficult. While several methods exist to assist researchers in eliciting consumer preferences, the vast majority relies upon conscious input from the potential consumers themselves. More critically, these methods do not afford researchers the ability to understand the cognitive mechanisms underlying what someone may be feeling or thinking while these preference judgments are being made. In this work, functional magnetic resonance imaging (fMRI) is used to investigate the neural processes behind multi-attribute product preference judgments. In particular, this work centers on uncovering unique features of sustainable preference judgments: preference judgments that involve products for which the environmental impact is a known quantity. This work builds upon earlier work that investigated how preference judgments are altered in the context of sustainability. A deeper look at participant decision making at the time of judgment is examined using neuroimaging with the goal of providing actionable insights for designers and product developers.

Third special issue on knowledge discovery and business intelligence

Third special issue on knowledge discovery and business intelligence