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A context‐aware system is a system that uses context to provide relevant information or services to its users. While there has been a variety of context middleware infrastructures and context‐aware applications, little work has been done on integrating context into database management systems. The purpose of this paper is to consider a preference database system that supports context‐aware queries, that is, queries whose results depend on the context at the time of their submission.

The paper proposes using data cubes to store the dependencies between context‐dependent preferences and database relations and on‐line analytical processing techniques for processing context‐aware queries. This allows for the manipulation of the captured context data at various levels of abstraction, for instance, in the case of a context parameter representing location, preferences can be expressed, for example, at the level of a city, the level of a country or both. To improve query performance, the paper uses an auxiliary data structure, called context tree. The context tree stores results of past context‐aware queries indexed by the context of their execution. Finally, the paper outline the implementation of a prototype context‐aware restaurant recommender.

The use of context is important in many applications such as pervasive computing where it is important that users receive only relevant information.

Although there is much research on location‐aware query processing in the area of spatial‐temporal databases, integrating other forms of context in query processing is a rather new research topic.

The work environment has been identified to influence employee satisfaction and work performance. In order to develop and provide work environments that meet the preferences of as many employees as possible, more information about user preferences and possible preference differences between different kinds of users is required. The purpose of this paper is to increase the understanding concerning office users' work environment preferences. The aim is to investigate whether there are differences in the preferences of office users based on their age, gender, their mobility, and whether they work individually or with others.

Office users' work environment preferences are studied through a survey directed to office employees. Statistical analysis is used in order to identify work environment preference differences between respondents of different age, gender, and the way they work.

The results indicate that there are differences between office users' work environment preferences concerning some characteristics of the work environment. The results show that the preferences vary both based on demographic issues such as age and gender as well as based on how they work.

The research is limited to the Helsinki Metropolitan Area, Finland, so the cultural context has to be taken into account when generalising the results.

The paper provides several stakeholders, such as user organisations, designers, consultants, and investors, valuable information on what kind of work environments office users prefer.

Food plays an important role in every culture around the world. Recently, cuisine preference analysis has become a popular research topic. However, most of these studies are conducted through questionnaires and interviews, which are highly limited by the time, cost and scope of data collection, especially when facing large-scale survey studies. Some researchers have, therefore, attempted to mine cuisine preferences based on online recipes, while this approach cannot reveal food preference from people’s perspective. Today, people are sharing what they eat on social media platforms by posting reviews about the meal, reciting the names of appetizers or entrees, and photographing as well. Such large amount of user-generated contents (UGC) has potential to indicate people’s preferences over different cuisines. Accordingly, the purpose of this paper is to explore Chinese cuisine preferences among online users of social media.

Based on both UGC and online recipes, the authors first investigated the cuisine preference distribution in different regions. Then, dish preference similarity between regions was calculated and few geographic factors were identified, which might lead to such regional similarity appeared in our study. By applying hierarchical clustering, the authors clustered regions based on dish preference and ingredient usage separately.

Experimental results show that, among 20 types of traditional Chinese cuisines, Sichuan cuisine is most favored across all regions in China. Geographical proximity is the more closely related to differences of regional dish preference than climate proximity.

Different from traditional definitions of regions to which cuisine belong, the authors found new association between region and cuisine based on dish preference from social media and ingredient usage of dishes. Using social media may overcome problems with using traditional questionnaires, such as high costs and long cycle for questionnaire design and answering.

Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings and item contents in making preference predictions. This study aims to improve news recommendation by proposing a GAN-based news recommendation model considering both ratings (implicit feedback) and the latent features of news content.

The collaborative topic modeling (CTM) can improve user preference prediction by combining matrix factorization (MF) with latent topics of item content derived from latent topic modeling. This study proposes a novel hybrid news recommendation model, Hybrid-CFGAN, which modifies the architecture of the CFGAN model with enhanced preference learning from the CTM. The proposed Hybrid-CFGAN model contains parallel neural networks – original rating-based preference learning and CTM-based preference learning, which consider both ratings and news content with user preferences derived from the CTM model. A tunable parameter is used to adjust the weights of the two preference learnings, while concatenating the preference outputs of the two parallel neural networks.

This study uses the dataset collected from an online news website, NiusNews, to conduct an experimental evaluation. The results show that the proposed Hybrid-CFGAN model can achieve better performance than the state-of-the-art GAN-based recommendation methods. The proposed novel Hybrid-CFGAN model can enhance existing GAN-based recommendation and increase the performance of preference predictions on textual content such as news articles.

As the existing CFGAN model does not consider content information and solely relies on history logs, it may not be effective in recommending news articles. Our proposed Hybrid-CFGAN model modified the architecture of the CFGAN generator by adding a parallel neural network to gain the relevant information from news content and user preferences derived from the CTM model. The novel idea of adjusting the preference learning from two parallel neural networks – original rating-based preference learning and CTM-based preference learning – contributes to improve the recommendation quality of the proposed model by considering both ratings and latent preferences derived from item contents. The proposed novel recommendation model can improve news recommendation, thereby increasing the commercial value of news media platforms.

Aspects extracted from the user’s historical records are widely used to define user’s fine-grained preferences for building interpretable recommendation systems. As the aspects were extracted from the historical records, the aspects that represent user’s negative preferences cannot be identified because of their absence from the records. However, these latent aspects are also as important as those aspects representing user’s positive preferences for building a recommendation system. This paper aims to identify the user’s positive preferences and negative preferences for building an interpretable recommendation.

First, high-frequency tags are selected as aspects to describe user preferences in aspect-level. Second, user positive and negative preferences are calculated according to the positive and negative preference model, and the interaction between similar aspects is adopted to address the aspect sparsity problem. Finally, an experiment is designed to evaluate the effectiveness of the model. The code and the experiment data link is: https://github.com/shiyu108/Recommendation-system

Experimental results show the proposed approach outperformed the state-of-the-art methods in widely used public data sets. These latent aspects are also as important as those aspects representing the user’s positive preferences for building a recommendation system.

This paper provides a new approach that identifies and uses not only users’ positive preferences but also negative preferences, which can capture user preference precisely. Besides, the proposed model provides good interpretability.

This paper aims to illuminate mechanisms through which memorable experiences with brands create lasting preferences. It is based on the proposition that intense positive (negative) affective consumption in the consumer’s youth creates powerful imprints, which influence brand preference (distaste) throughout life.

Autobiographical memories with Nutella are retrieved from three different user groups, i.e. heavy-, light- and non-users. The retrieved memory narratives are analysed using conditioning theory, i.e. operant, classical or no conditioning are identified and compared across groups.

The research’s central proposition is affirmed, yet the dominant form of conditioning mechanism differs per group. Operant conditioning outperforms classical conditioning in creating strong and lasting preferences. Heavy- and non-users predominantly exhibit in-tensely positive and negative operant conditioning, respectively. Light-users on the other hand recall less affectively intense consumption experiences, mainly featuring classical conditioning. The light-users’ recollections suggest a mere exposure effect to be more appropriate in describing the preference formation in this user group.

Users not having experienced affectively intense consumption, i.e. light-users, are likely to be influenced in their preference over time through other factors, which this paper does not focus on.

Memory elicitation and exploration provides valuable insights to shape both promotional as well as advertising strategies.

The study extends existing theory on conditioning in marketing by first using a novel qualitative approach to analyse conditioning procedures in real-life settings, and second, it highlights operant conditioning’s superior ability in creating lasting preferences.

This study aims to review the existing methods for additive manufacturing (AM) process selection and evaluate their suitability for design for additive manufacturing (DfAM). AM has experienced a rapid development in recent years. New technologies, machines and service bureaus are being brought into the market at an exciting rate. While user’s choices are in abundance, finding the right choice can be a non-trivial task.

AM process selection methods are reviewed based on decision theory. The authors also examine how the user’s preferences and AM process performances are considered and approximated into mathematical models. The pros and cons and the limitations of these methods are discussed, and a new approach has been proposed to support the iterating process of DfAM.

All current studies follow a sequential decision process and focus on an “a priori” articulation of preferences approach. This kind of method has limitations for the user in the early design stage to implement the DfAM process. An “a posteriori” articulation of preferences approach is proposed to support DfAM and an iterative design process.

This paper reviews AM process selection methods in a new perspective. The users need to be aware of the underlying assumptions in these methods. The limitations of these methods for DfAM are discussed, and a new approach for AM process selection is proposed.

The purpose of this paper is to examine the preference of office property users for green features in Lagos, Nigeria. This is with a view to determining the degree of users’ aspiration for green buildings in the country.

The study purposively sampled two office properties from the management portfolio of 88 registered estate firms in Lagos. Data were collected using self-administered questionnaire on two users purposively selected from each of the properties. The data were analyzed with the use of frequency distribution, percentages and measures of the users’ preference index.

The results revealed that the preference for green features by office property users in the study area was above average (2.5 on a five-point scale). Feature relating to “building ecology, waste and recycling” is the most preferred feature with UPI of 3.970 while those relating to “owner and occupant education” with UPI of 3.558 were least in preference.

The paper concludes that with the preference of users for green features in the study area, it may be necessary for government to strengthen the existing framework for sustainable development. Also, increased sensitization of investors, users, professionals and other stakeholders in the building industry is pertinent to the success of green building practice in the country.

This is one of the few studies on users’ preference for green features in emerging economy, particularly in the Nigerian context.

Due to the rapidly increasing volume of users and products in virtual worlds, recommender systems are an important feature in virtual worlds; they can help solve information overload problems. Virtual world users are able to perform several actions that promote the enjoyment of their virtual life, including interacting with others, visiting virtual houses and shopping for virtual products. This study aims to concentrate on the following two important factors: the social neighbors’ influences and the virtual house bandwagon phenomenon, which affects users’ preferences during their virtual house visits and purchasing processes.

The authors determine social influence by considering the interactions between the target user and social circle neighbors. The degree of influence of the virtual house bandwagon effect is derived by analyzing the preferences of the virtual house hosts who have been visited by target users during their successive visits. A novel hybrid recommendation method is proposed herein to predict users’ preferences by combining the analyses of both factors.

The recommendation performance of the proposed method is evaluated by conducting experiments with a data set collected from a virtual world platform. The experimental results show that the proposed method outperforms the conventional recommendation methods, and they also exhibit the effectiveness of considering both the social influence and the virtual house bandwagon effect for making effective recommendations.

Existing studies on recommendation methods did not investigate the virtual house bandwagon effects that are unique to the virtual worlds. The novel idea of the virtual house bandwagon effect is proposed and analyzed for predicting users’ preferences. Moreover, a novel hybrid recommendation approach is proposed herein for generating virtual product recommendations. The proposed approach is able to improve the accuracy of preference predictions and enhance the innovative value of recommender systems for virtual worlds.

Building circulation has an important impact on human comfort of buildings and is one of the critical factors in building design. A quantitative walkability evaluation of building circulation can benefit both building design and operation. However, indoor walkability of building circulation is determined not only by objective path features but also by subjective user preference. How to incorporate the preference from a large group of users into the design process is still a challenging issue.

This study proposes a participatory framework of indoor path walkability evaluation based on user preference. Hierarchical indicators are developed to objectively measure indoor path features. Furthermore, group decision-making theory is adopted to aggregate individual user preference into user common preference for determining the relative indicator weights. Finally, integrated walkability scores (IWSs) are calculated to evaluate indoor path walkability quantitatively.

A total of three case scenarios demonstrate that the proposed evaluation framework provides an efficient way for designers and owners to measure user preference quantitatively, analyze building circulations based on user preference and compare the walkability of different building design schemes.

The developed methods provide an efficient way for designers and owners to measure user preference quantitatively, analyze building circulations based on user preference and compare the walkability of different building design schemes.

This study develops a comprehensive and quantitative walkability evaluation approach that considers both objective path features and subjective user preference derived from user characteristics and walking purposes, which provides an effective way to incorporate user feedback into the building design process and operation.