Semantics-aware content-based recommender systems: Design and architecture guidelines

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

Recommender systems fight information overload by selecting automatically items that match the personal preferences of each user. Content-based recommenders suggest items similar to those the user liked in the past by resorting to syntactic matching techniques, which leads to overspecialized recommendations. The so-called collaborative approaches fight this problem by considering the preferences of other users, which results in new limitations. In this paper, we avoid the intrinsic downsides of collaborative solutions and diversify the content-based recommendations by reasoning about the semantics of the user’s preferences. Specifically, we present a novel domain-independent content-based recommendation strategy that exploits Spreading Activation techniques as the reasoning mechanism. Our contribution consists of adapting and extending the internals of traditional SA techniques in order to fulfill the personalization requirements of a recommender system. The resulting reasoning-driven strategy enables to discover additional knowledge about the user’s preferences and leads to more accurate and diverse content-based recommendations. Our approach has been preliminary validated with a set of viewers who received recommendations of Digital TV contents.KeywordsRelevance FeedbackSpreading ProcessVirtual LinkSemantic AssociationOntology ServerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Content-based recommender systems use preference ratings and features that characterize media to model users' interests or information needs for making future recommendations. While previously developed in the music and text domains, we present an initial exploration of content-based recommendation for spoken documents using a corpus of public domain internet audio. Unlike familiar speech technologies of topic identification and spoken document retrieval, our recommendation task requires a more comprehensive notion of document relevance than bags-of-words would supply. Inspired by music recommender systems, we automatically extract a wide variety of content-based features to characterize non-linguistic aspects of the audio such as speaker, language, gender, and environment. To combine these heterogeneous information sources into a single relevance judgement, we evaluate feature, score, and hybrid fusion techniques. Our study provides an essential first exploration of the task and clearly demonstrates the value of a multisource approach over a bag-of-words baseline.

As e-commerce offers more and more choices for users, its structure becomes more and more complicated. Inevitably, it brings about the problem of information overload. The solution to this problem is an e-commerce personalized recommendation system using machine learning technology. People often seem confused when facing extensive information and cannot grasp the key points. This paper studies the personalized recommendation technology of e-commerce: deeply analyzes the related technologies and algorithms of the e-commerce recommendation system and proposes the latest architecture of the e-commerce recommendation system according to the current development status of the e-commerce recommendation system. The system recommends accuracy and real-time requirements and divides the system into two parts: offline mining and online recommendation and analyzes and implements the functions and technologies of each part. User-based recommender systems, collaborative filtering recommender systems, and content-based recommender systems are analyzed, respectively. The personalized recommendation cannot only quickly help customers find the required commodity information in a wide range of complex information but also can compare more commodity information to help customers to judge. However, the existing recommendation system has some problems such as the lack of recommendation personality, the reduced relevance of recommendation, and the poor timeliness of recommendation. Finally, a recommendation system that combines three recommendation algorithms is designed, and experiments are carried out. The newly designed recommendation system is compared with three different recommendation systems, and a summary and outlook are made. Based on the introduction of the relevant theories, characteristics, and mainstream technologies of personalized recommendation based on machine learning, this document presents a constructive example of a model based on the factors that influence personalized e-commerce information recommendations in the retail sector. Through questionnaire surveys, we analyze and design the influencing factors for consumers to purchase personalized products after the survey and build a project using state-of-the-art field learning techniques. Through the model to test the eight hypotheses proposed in this paper, the results show that customer income level, customer online shopping experience, commodity prices, product quality, recommendation relevance, credit evaluation, and service quality will have a significant positive impact on shopping willingness and ultimately affect the customer’s shopping behavior. e-commerce platform can use this influencing factor to establish personalized information recommendation service mode.

Sedentary lifestyles and bad eating habits influence the onset of many serious health problems. Healthy behavior change is an arduous task, and requires a careful planning. In this thesis, we propose that behavior recommenders can help their users achieve healthy behavior change. Such a system should inspire its users with small, incremental and achievable goals. For this, it must resolve a trade-off between two opposing objectives: help the user achieve a steady improvement in target behavior, and avoid extreme goals that may injure or discourage the user. This is an unprecedented challenge in the recommender systems research. If the system understands the impacts of past interventions for behavior change, it can determine its usersâ behavioral responses to its own recommendations. This implies a specific data curation, in which we not only measure people's behavior but also deliberately introduce an intervention to monitor its effect on people's patterns. In turn, the system can use these existing users' information to derive the right procedure for effective recommendations. In this study we capitalize on this insight and develop InspiRE - our behavior recommender framework. Through InspiRE we propose the following contributions: 1) We design the data curation. 2) We develop the novel approaches for behavior profiling 3) We develop an evaluation process for this novel type of recommender system, and also compare it with traditional, similarity-based recommendation approach. We curate a dataset that contains information of daily step counts and social intervention for 83 people. InspiRE successfully uses the observations from this dataset, and proposes recommendations that are both effective and feasible. We also show that InspiRE can generalize to other dimensions of well being: we demonstrate this through a dataset that contains the snacking patterns of 73 people, who receive message-based interventions. We observe that InspiRE's recommendation strategy is in line with theories of behavior change.

Recommender systems have the effect of guiding users in a personal- ized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recom- mender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the basic concepts and terminology of content- based recommender systems, a high level architecture, and their main advantages and drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describ- ing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and future research which might lead towards the next generation of systems, by describing the role of User Generated Content as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.

Recommender systems are frequently used as part of online shops to help consumers browse through large product offerings by recommending those products which are the most relevant for them. Although consumers’ interactions with recommender systems have been subject to substantial research, it is still unclear what the effect on aggregated sales diversity is, i.e. whether this leads to predominance of fast-selling or niche products. It is also unclear, whether any potential effects would differ between specific recommender technologies. We created a realistic web-experiment to monitor consumer behavior while purchasing digital music tracks when different recommender technologies are present. To analyze potential changes in sales diversity we used the Gini coefficient as well as additional measures. We found that sales diversity increases for all recommender technologies, except for bestseller lists. Furthermore, the differences across recommender technologies are rather small. Our findings have significant implications for online retailers and for producers.

Recently, online shopping has become very prominent. Furthermore, most e-commerce platforms use recommender systems to suggest products to the user for potential purchase to help enhance the user experience. Existing recommender models determine the correlation between user profiles and product information to make product recommendations. Although current recommender systems can accurately suggest products, there is a lack of transparency in terms of how the recommendations are made. As a result, users may not trust the suggestions made by what could be perceived as a black box. To address this problem, we have designed a deep recommender system that discovers user preferences and provides the reasons behind the recommendations it makes by examining clues such as user reviews and item descriptions. We adopt the attention mechanism to enhance text analysis capability, which not only enables accurate recommendations but also shows the relationship between products and user profiles. Furthermore, we conduct a case study in which we utilize feature weights to present the reasons to the user by observing attention maps. The results of our experiment show that our model can consistently provide accurate recommendations and justifications to the user. Moreover, our model can handle new items and users, that is, data that is not part of the initial training dataset. Our recommender system is a novel solution that leverages the advantages of content-based and collaborative filtering.

Mining people's semantic trajectory behaviours from geotagged photographs

A location-based orientation-aware recommender system using IoT smart devices and Social Networks

The explosive growth of the Internet has led to an unprecedented increase in available information, creating a major challenge in identifying content that aligns with user interests. Recommendation systems (RS) address this challenge by providing personalized content recommendations based on user behavior and preferences. This article systematically reviews the evolution of recommendation systems with a focus on the methodologies of machine learning (ML) and deep learning (DL). It highlights core technologies in ML-based RS, such as content-based recommendation, collaborative filtering (CF), and hybrid filtering (HF). The article evaluates the effectiveness of various DL models, including autoencoders (AE), convolutional neural networks (CNN), and recurrent neural networks (RNN). The review also addresses key challenges faced by RS, such as cold start issues and the need for improved model transparency and interpretability. DL methods are shown to significantly enhance recommendation accuracy by leveraging complex patterns in large-scale data, to improve the user experience. Future research directions include refining data preprocessing, enhancing feature engineering, compressing and accelerating DL models, and improving interpretability through advanced mechanisms. This study provides valuable insights and references for researchers aiming to advance recommendation system design and performance optimization.

I. ABSTRACT In Internet of Things (IoT), selling smart physical objects together with a compatible mobile app becomes an upcoming trend. The app allows to control or monitor the physical object and its sensors in an easy, ubiquitous, and user friendly way. In this post, we present a novel recommender system which uses publicly available data about these apps as a source for personalization. The proposed system infers user’s physical objects from exploring the installed apps on her mobile devices like smartphones and tablets and builds a digital inventory of physical objects of each user. Then, these inventories can be used to create personalized recommendations. A. Background Smartphones have become our daily companions and support us in almost any situation of our daily life. We use them for communication, gaming, news reading, watching movies, payment, mobile banking and many others. The way we use smartphones is correlated with individuals’ needs, interests, habits, and personality [1]. Custom-tailored content like recommendations, advertisement, personalized prices and search results can be presented to consumers, based on observed activities on their devices [2]. From the perspective of IoT, smartphones are becoming increasingly important because they are used as hubs between the physical objects and Internet. For instance, smartphones provide dashboards to manage our smart home appliance (like heating system, light-bulb, key, TV) as well as retrieving and presenting data from our smart gadgets (like pedometers, body sensors). Since there is no method to automatically get a list of individuals’ physical objects, offering fitting recommendations in IoT is difficult. Recent studies propose to gather data about installed apps on smartphones as a way to get valuable user information and to derive user profiles [3]. We believe that the installed apps are also a door opener for gathering information about the connected physical objects and we are able to automatically

We demonstrate how crowdsourcing can be used to automatically build a personalized tourist attraction recommender system, which tailors recommendations to specific individuals, so different people who use the system each get their own list of recommendations, appropriate to their own traits. Recommender systems crucially depend on the availability of reliable and large scale data that allows predicting how a new individual is likely to rate items from the catalog of possible items to recommend. We show how to automate the process of generating this data using crowdsourcing, so that such a system can be built even when such a dataset is not initially available. We first find possible tourist attractions to recommend by scraping such information from Wikipedia. Next, we use crowdsourced workers to filter the data, then provide their opinions regarding these items. Finally, we use machine learning methods to predict how new individuals are likely to rate each attraction, and recommend the items with the highest predicted ratings.

Acoustic noise induced by multilayer ceramic capacitors (MLCCs) in power distribution network (PDN) is a critical issue regarding product user experience. In this work, design guidelines for decoupling capacitor selection, layout geometries are proposed targeting the acoustic noise, through analysis of the acoustic noise generation mechanism in printed circuit board (PCB). A test board is designed to validate the proposed design guidelines. With sound pressure level measurement of the test vehicle, the effectiveness of the proposed design guidelines is confirmed. In general, the decoupling capacitor layout design guidelines for acoustic noise consideration are consistent with the requirements for PDN to achieve high electrical performance.

For distributed multi-user and multi-relay cooperative networks, the received signal may be affected by multiple timing offsets (MTOs) and multiple channels that need to be jointly estimated for successful decoding at the receiver. This paper addresses the design of optimal training sequences for efficient estimation of MTOs and multiple channel parameters. A new hybrid Cramer-Rao lower bound (HCRB) for joint estimation of MTOs and channels is derived. Subsequently, by minimizing the derived HCRB as a function of training sequences, three training sequence design guidelines are derived and according to these guidelines, two training sequences are proposed. In order to show that the proposed design guidelines also improve estimation accuracy, the conditional Cramer-Rao lower bound (ECRB), which is a tighter lower bound on the estimation accuracy compared to the HCRB, is also derived. Numerical results show that the proposed training sequence design guidelines not only lower the HCRB, but they also lower the ECRB and the mean-square error of the proposed maximum a posteriori estimator. Moreover, extensive simulations demonstrate that application of the proposed training sequences significantly lowers the bit-error rate performance of multi-relay cooperative networks when compared to training sequences that violate these design guidelines.