Content based recommender system by using eye gaze data

Implicit relevance feedback (IRF) is the process by which a search system unobtrusively gathers evidence on searcher interests from their interaction with the system. IRF is a new method of gathering information on user interest and, if IRF is to be used in operational IR systems, it is important to establish when it performs well and when it performs poorly. In this paper we investigate how the use and effectiveness of IRF is affected by three factors: search task complexity, the search experience of the user and the stage in the search. Our findings suggest that all three of these factors contribute to the utility of IRF.

E-commerce Recommender Systems suggest useful and interesting products to customers in order to increase user satisfaction and online conversion rates. They typically exploit explicit or implicit user feedback such as ratings, buying records or clickstream data and apply statistical methods to derive recommendations. Collaborative filtering is one of the most widely used approaches in recommender systems. However, the traditional collaborative filtering methods compute users’ similarities in the dimension of products, and they do not take the influence of neighbor users into consideration when computing such similarities. This paper focuses on explicitly formulated user requirements as the sole type of user feedback. The new collaborative filtering method based on user interests’ transmission. This method computes customers’ similarities in the dimension of interests, and takes the interests transmission between different customers into reflection. This method not only can cope with cold start problem and data sparse problem, but also have prediction precision.

Nowadays, virtual learning environments have become widespread to avoid time and space constraints and share high-quality learning resources. As a result of human–computer interaction, student behaviors are recorded instantly. This work aims to design an educational recommendation system according to the individual's interests in educational resources. This system is evaluated based on clicking or downloading the source with the help of the user so that the appropriate resources can be suggested to users. In online tutorials, in addition to the problem of choosing the right source, we face the challenge of being aware of diversity in users' preferences and tastes, especially their short-term interests in the near future, at the beginning of a session. We assume that the user's interests consist of two parts: (1) the user's long-term interests, which include the user's constant interests based on the history of the user's dynamic activities, and (2) the user's short-term interests, which indicate the user's current interests. Due to the use of Bilstm networks and their gradual learning feature, the proposed model supports learners' behavioral changes. An average accuracy of 0.9978 and a Loss of 0.0051 offer more appropriate recommendations than similar works.

Предметом вивчення в статті є процеси формування рекомендованого списку товарів та послуг в ситуації циклічного холодного старту рекомендаційної системи. Така ситуація характеризується циклічною зміною інтересів користувачів, що потребує уточнення вхідних даних для побудови рекомендацій. Метою є розробка методу доповнення вхідних даних для побудови рекомендацій непостійним користувачам, що змінюють свої вимоги, з використанням темпоральних обмежень типу «Next». Завдання: виділити базові особливості темпоральних залежностей в рекомендаційних системах; розробити концепцію коригування вхідних даних у ситуації циклічного холодного старту; розробити метод доповнення вхідних даних на основі темпоральних обмежень типу «Next». Отримані наступні результати. Виконано структуризацію темпоральних залежностей з урахуванням особливості їх застосування в рекомендаційних системах. Показано, що для опису поведінки користувача в таких системах можна використовувати залежності типу «Next» між послідовним у часі вибором одного й того ж об’єкту, а також залежності типу «Future» з проміжним вибором інших товарів або послуг. Запропоновано концептуальний підхід до уточнення вхідних даних на основі темпоральних залежностей. В рамках представленого концептуального підходу запропоновано метод доповнення вхідних даних рекомендаційної системи на основі формування темпоральних обмежень типу «Next». Висновки. Наукова новизна отриманих результатів полягає в наступному. Запропоновано метод доповнення вхідних даних рекомендаційної системи в ситуації циклічного холодного старту з використанням темпоральних обмежень типу «Next». Метод містить етапи узагальнення вхідних даних, формування темпоральних обмежень типу «Next», а також доповнення вхідних даних згідно отриманих обмежень. Запропонований метод дозволяє підвищити ефективність побудови рекомендацій для непостійних користувачів на основі формування обмежень, що відображають обов’язкові зміни інтересів відомих користувачів.

Explicit and implicit feedback exhibits different characteristics of users' preferences with both pros and cons. However, a combination of these two types of feedback provides another paradigm for recommender systems (RS). Their combination in a user preference model presents a number of challenges but can also overcome the problems associated with each other. In order to build an effective RS on combination of both types of feedback, we need to have comparative data allowing an understanding of the computation of user preferences. In this paper, we provide an overview of the differentiating characteristics of explicit and implicit feedback using datasets mined from Last.fm, an online music station and recommender service. The datasets consisted of explicit positive feedback (by loving tracks) and implicit feedback which is inherently positive (the number of times a track is played). Rather than relying on just one type of feedback, we present techniques for extracting user preferences from both. In order to compare and contrast the performances of these techniques, we carried out experiments using the Taste recommender system engine and the Last.fm datasets. Our results show that implicit and explicit positive feedback complements each other, with similar performances despite their different characteristics.

Click-through rate (CTR) prediction plays a critical role in recommender systems and other applications. Recently, modeling user behavior sequences attracts much attention and brings great improvements in the CTR field. Many existing works utilize attention mechanism or recurrent neural networks to exploit user interest from the sequence, but fail to recognize the simple truth that a user's real-time interests are inherently diverse and fluid. In this paper, we propose DisenCTR, a novel dynamic graph-based disentangled representation framework for CTR prediction. The key novelty of our method compared with existing approaches is to model evolving diverse interests of users. Specifically, we construct a time-evolving user-item interaction graph induced by historical interactions. And based on the rich dynamics supplied by the graph, we propose a disentangled graph representation module to extract diverse user interests. We further exploit the fluidity of user interests and model the temporal effect of historical behaviors using Mixture of Hawkes Process. Extensive experiments on three real-world datasets demonstrate the superior performance of our method comparing to state-of-the-art approaches.

The rapid development of Internet and mobile communication, smart phone, tablet computer and other mobile devices become widespread, they have become an indispensable part of life, changing the human lifestyle, way of working and learning style, it brings great convenience and fun to us. This paper mainly discusses the status of personalized recommendation system, mobile application and analyse the main problems of recommendation system. This research proposes a rising mobile application domain, which uses web crawling technique to get explicit feedback from a real-world mobile application market. The explicit feedback is the users’ ratings data on Apps and the corresponding category information. To establish a mobile application recommendation model based on user interest and categorization information and propose a novel collective matrix factorization algorithm. Through the crawled data, the improved algorithm, and the traditional collaborative. The traditional mobile application recommendation systems are mostly based on keyword search, popularity, download usage and categories, thus they do not make personalized recommendation to users, and invalid to find applications that users are interested in. Through the user’s historical behaviour, a personalized recommendation system can be established to recommend the applications that fit user’s interests effectively. This paper is based on the collective matrix factorization using user interest such as user’s ratings and category information, designs a personalized recommendation system for mobile applications by using the relationship between them.

Online forums enable users to discuss together around various topics. One of the serious problems of these environments is high volume of discussions and thus information overload problem. Unfortunately without considering the users interests, traditional Information Retrieval (IR) techniques are not able to solve the problem. Therefore, employment of a Recommender System (RS) that could suggest favorite's topics of users according to their tastes could increases the dynamism of forum and prevent the users from duplicate posts. In addition, consideration of semantics can be useful for increasing the performance of IR based RS. Our goal is study of impact of ontology and data mining techniques on improving of content-based RS. For this purpose, at first, three type of ontologies will be constructed from the domain corpus with utilization of text mining, Natural Language Processing (NLP) and Wordnet and then they will be used as an input in two kind of RS: one, fully ontology-based and one with enriching the user profile vector with ontology in vector space model (VSM) (proposed method). Afterward the results will be compared with the simple VSM based RS. Given results show that the proposed RS presents the highest performance.

Although relevance feedback (RF) has been extensively studied in the content-based image retrieval community, no commercial Web image search engines support RF because of scalability, efficiency, and effectiveness issues. In this paper, we propose a unified relevance feedback framework for Web image retrieval. Our framework shows advantage over traditional RF mechanisms in the following three aspects. First, during the RF process, both textual feature and visual feature are used in a sequential way. To seamlessly combine textual feature-based RF and visual feature-based RF, a query concept-dependent fusion strategy is automatically learned. Second, the textual feature-based RF mechanism employs an effective search result clustering (SRC) algorithm to obtain salient phrases, based on which we could construct an accurate and low-dimensional textual space for the resulting Web images. Thus, we could integrate RF into Web image retrieval in a practical way. Last, a new user interface (UI) is proposed to support implicit RF. On the one hand, unlike traditional RF UI which enforces users to make explicit judgment on the results, the new UI regards the users' click-through data as implicit relevance feedback in order to release burden from the users. On the other hand, unlike traditional RF UI which hardily substitutes subsequent results for previous ones, a recommendation scheme is used to help the users better understand the feedback process and to mitigate the possible waiting caused by RF. Experimental results on a database consisting of nearly three million Web images show that the proposed framework is wieldy, scalable, and effective.

In this paper, we describe a new scheme to learn dynamic user's interests in an automated information filtering and gathering system running on the Internet. Our scheme is aimed to handle multiple domains of long-term and short-term user's interests simultaneously, which is learned through positive and negative user's relevance feedback. We developed a 3-descriptor approach to represent the user's interest categories. Using a learning algorithm derived for this representation, our scheme adapts quickly to significant changes in user interest, and is also able to learn exceptions to interest categories.

Recommender systems play an important role in most modern e-commerce applications. They have allowed users to become aware of the myriad choices available to them. The ease of information and the abundance of options have helped users make educated decisions. A recommender system studies a user's preferences and continues learning the user's changing interests, so as to suggest items that incline with the user's interests. In cases where a user is new to the application, or the user prefers not to discourse preferences, the recommender system is unable to gather the user's preference on any item. This is called the cold start problem; wherein the system can make valid recommendations only once the user starts informing the system about his/her choices. In this paper, we discuss the challenges faced by the cold start problem and how this problem may be alleviated using social media. We suggest an approach where we collect public information from users' social media accounts and analyze this information to understand their preferences. In particular, we gather the new user's information using their Twitter profile; i.e., the user's interest and preferences are extracted from his/her Twitter profile by analyzing his/her tweets. These interests will help the system understand what kind of movies the user will be most interested in. We compare these preferences with the metadata about the individual items. Using this approach, we develop a movie recommendation system wherein we produce top-N movie recommendations for a user. We used the MovieTweetings dataset to model the application. Two sets of results have been produced. In the first, smaller set of 770 users, 72.67% of users have received 100% accurate movie recommendations while nearly 80% of users got more than 75% accuracy. For the second, larger set of more than 3,500 users, 53 % of users have received 100% accurate recommendations while 72% of users got more than 75% accuracy. These encouraging results have demonstrated that the approach is effectively in alleviating cold start problems in recommendation systems, and may be applicable to many other e-commerce applications.

Implicit relevance feedback has received wide attention recently, as a means to capture the search context in improving search accuracy. However, implicit feedback is usually not available to public or even research communities at large for reasons like being a potential threat to privacy of web users. This makes it difficult to experiment and evaluate web search related research and especially web search personalization algorithms. Given these problems, we are motivated towards an artificial way of creating user relevance feedback, based on insights from query log analysis. We call this simulated feedback. We believe that simulated feedback can be immensely beneficial to web search engine and personalization research communities by greatly reducing efforts involved in collecting user feedback. The benefits from “Simulated feedback” are—It is easy to obtain and also the process of obtaining the feedback data is repeatable and customizable. In this chapter, we describe a simple yet effective approach for creating simulated feedback. We evaluated our system using clickthrough data of a set of real world users and achieved 65% accuracy in generating click-through data of those users.

Making a decision among a set of items from compound and complex information has been becoming a difficult task for common users. Collaborative filtering has been the mainstay of automatically personalized search employed in contemporary recommender systems. Until now, it is still a challenging issue to reduce the gap between user perception and multimedia contents. To bridge user's interests and multimedia items, in this paper, we present an intelligent multimedia recommender system by integrating annotation and association mining techniques. In our proposed system, low-level multimedia contents are conceptualized to support rule-based collaborative filtering recommendation by automated annotation. From the discovered relations between user contents and conceptualized multimedia contents, the proposed recommender system can provide a suitable recommendation list to assist users in making a decision among a massive amount of items.

Situation-Aware User’s Interest Prediction aims at enhancing the information retrieval (IR) capabilities by expanding explicit user requests with implicit user interests, to better meet individual user needs. However, not all user interests are the same in all situations, especially for the case of a mobile environment. Thus, user interests are complex, dynamic, changing, and even contradictory. Consequently, they should be adapted to the user’s specific search context. In this paper, we introduce a new approach that aims at building a dynamic representation of the semantic situation of ongoing mobile environment retrieval tasks. The semantic situation is then used to activate different classification rules of user’s past interests at run time. Doing so, the best interest class’s is proposed to expand the user’s request. Our approach makes use of a semantic enrichment using Dbpedia, providing enriched descriptions of the semantic situations involved for discovering user interests, and enabling the definition of effective means to related contexts. Carried out experiments, undertaken versus Google search, emphasize the relevance of our proposal and open many promising issues.

Session-based recommendation aims to predict the next item that is most likely to be clicked by an anonymous user, based on his/her clicking sequence within one visit. It becomes an essential function of many recommender systems since it protects privacy. However, as the accumulated session records keep increasing, it becomes challenging to model the user interests since they would drift when the time span is large. Efforts have been devoted to handling dynamic user interests by modeling all historical sessions at one time or conducting offline retraining regularly. These solutions are far from practical requirements in terms of efficiency and capturing timely user interests. To this end, we propose a memory-efficient framework - TASRec. It constructs a graph for each day to model the relations among items. Thus, the same item on different days could have different neighbors, corresponding to the drifting user interests. We design a tailored graph neural network to embed this dynamic graph of items and learn temporal augmented item representations. Based on this, we leverage a sequential neural architecture to predict the next item of a given sequence. Experiments on real-world datasets demonstrate that TASRec outperforms state-of-the-art session-based recommendation methods.