An adaptive algorithm for learning changes in user interests

In recommender systems, one of critical tasks is to help users find their interested items among huge amount of items. The development of recommendation methods and techniques has driven many real-world recommendation applications. Desirable recommendations are vital both to the target users and the recommended items. Mining meaningful interest information from different time spans is crucial for recommendation precision. In this paper, the relations between different time spans are adopted to construct the signed networks and imitate users interest changes. First, we initially utilize temporal score information to divide different time spans. Then, the signed networks are formed at different time steps referring to users interest changes. Furthermore, considering the characteristics of signed networks, we define a new similarity measurement to grasp the common interest between two adjacent signed networks. A new adjacent matrix is obtained by weighting the network adjacent matrices of different time steps. According to the constructed dynamic evolutionary clustering model in a signed network, the nodes are divided into different clusters. Finally, the predicted ratings are calculated in each subclass instead of the entire system, which reduces the computational cost greatly. The extensive simulations are conducted based on two real-world datasets, Movielens and CiaoDVD, for demonstrating that the recommend accuracy is significantly improved using our scheme.

This paper presents a novel method to retrieve images by learning the embedded visual concept from a set of given examples. Through a user's relevance feedback, the visual concept can be effectively learned to classify images which contain common visual entities. The learning process is started by providing a set of either positive or negative training examples and is then interactively adjusted according to the user's relevance feedback. In contrast to traditional methods, the proposed method utilizes a novel way to overcome the under-training problem which is frequently suffered in the learning process. Since no time-consuming optimization process is involved, the proposed method learns the visual concepts extremely fast. Therefore, the target concept can be learned on-line and is user-adaptable for effective retrieval of image contents. Experimental results are provided to prove the superiority of the proposed method.

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.

With the rapid growing volume of images in medical databases, development of efficient image retrieval systems to retrieve relevant or similar images to a query image has become an active research area. Despite many efforts to improve the performance of techniques for accurate image retrieval, its success in biomedicine thus far has been quite limited. This article presents an adaptive content-based image retrieval (CBIR) system for improving the performance of image retrieval in mammographic databases. In this work, the authors propose a new relevance feedback approach based on incremental learning with support vector machine (SVM) regression. Also, the authors present a new local perturbation method to further improve the performance of the proposed relevance feedback system. The approaches enable efficient online learning by adapting the current trained model to changes prompted by the user's relevance feedback, avoiding the burden of retraining the CBIR system. To demonstrate the proposed image retrieval system, the authors used two mammogram data sets: A set of 76 mammograms scored based on geometrical similarity and a larger set of 200 mammograms scored by expert radiologists based on pathological findings. The experimental results show that the proposed relevance feedback strategy improves the retrieval precision for both data sets while achieving high efficiency compared to offline SVM. For the data set of 200 mammograms, the authors obtained an average precision of 0.48 and an area under the precision-recall curve of 0.79. In addition, using the same database, the authors achieved a high pathology matching rate greater than 80% between the query and the top retrieved images after relevance feedback. Using mammographic databases, the results demonstrate that the proposed approach is more accurate than the model without using relevance feedback not only in image retrieval but also in pathology matching while maintaining its effectiveness for online relevance feedback applications.

Search engines usually return relevant sorted results based on the keywords. Because of the lack of considering the user's current search interest and intention, this kind of strategy may not meet users' personalized search requirements. In order to retrieve results associated with the user's current search interest, existing researches integrate the user's interest information into the search process and ranking, so as to make the search results fit to the user's current search target more better. Different from the unified model of interest which most current related researches used, we propose a model of combing user's interests for the purpose of ranking in the light of different query types. This paper first classified queries into several types based on the current keywords and the sources of user's interests, such as short-term interest in user clicks, and long-term interest in user's search history, search logs of the search system etc. Then based on the sources of interests, we established a model of combination of interests which based on the artificial neural network and was trained by related approaches and data sets. Depending on different query types, we combined these sources of user's interests that the most best reflecting the user's current search intention to enhance the user's search experience. Comparing with the existing model of single interest, the results of the experiments show that our model can obtain better ranking on the results.

The sequential recommendation selects and recommends next items for users by modeling their historical interaction sequences, where the chronological order of interactions plays an important role. Most sequential recommendation methods only pay attention to the order information among the interactions and ignore the time intervals information, which leads to the limitations of capturing dynamic user interests. And previous work neglects diversity in order to improve recommendation accuracy. The model Temporal Self-Attention and Multi-Preference Learning (TSAMPL) is proposed to improve sequential recommendation, which learns dynamic and general user interests separately. The proposed temporal gate self-attention network is introduced to learn dynamic user interests, which takes both contextual information and temporal dynamics into account. To model general user interests, we employ a multi-preference matrix to learn users’ multiple types of preferences for improving recommendation diversity. Finally, the interest fusion module combines dynamic user interests (accuracy) and general user interests (diversity) adaptively. The experiments in sequential recommendation confirm our method is superior to all comparison methods, we also study the impact of each component in the model.

Current short video recommendation systems face challenges in accurately identifying user interests due to large-scale, heterogeneous user data, as well as addressing cold-start problems when encountering new users and content. This study proposes a deep learning and reinforcement learning (DLRL) framework to enhance recommendation performance. User and video data are collected and preprocessed using the Hadoop platform, while automated metadata extraction tools, optical character recognition (OCR), and audio-to-text technologies extract multimodal features, including subtitles and audio content. A convolutional neural network (CNN) extracts semantic and visual features from text-image data, while a long short-term memory (LSTM) network captures temporal dependencies in user behavior to detect interest changes. A multimodal attention mechanism integrates these diverse features to form comprehensive user portraits, and a deep neural network (DNN) reduces feature dimensionality to represent user preferences accurately. The recommendation process is formulated as a reinforcement learning task optimized through Deep Q Network (DQN) and Proximal Policy Optimization (PPO) algorithms. Experimental results demonstrate that the proposed approach achieves superior click-through rates (6.22%), viewing duration (73.31 s), accuracy (0.877), and recall (0.858), effectively overcoming cold-start limitations and dynamically adapting to evolving user interests. This shows that combining DLRL can optimize the performance of short video recommendations, improve accuracy and experience, and address the issues of cold start and capturing changes in user interests.

Target tracking is a hot topic in the field of computer vision. It uses the context information of video or image sequence to model the appearance and motion information of the target, so as to predict the motion state of the target and calibrate the position of the target. From the perspective of building models, target tracking algorithms can be divided into generative models and discriminant models; From the number of tracking targets, it can be divided into single target tracking and multi-target tracking. Target tracking integrates the theories and algorithms of image processing, machine learning, optimization and other fields. It is the premise and foundation to complete higher-level image understanding.The basic task of target tracking is to give the initial position of the target in a video sequence and continuously track and locate the target in each subsequent frame. In this process, a priori conditions such as the color, shape and size of the target will not be provided, that is, the tracking algorithm can track only by learning the target in the first frame. The traditional solution is to calculate the project similarity offline and save it in the system for algorithm call, but it can not make full use of the latest scoring data to reflect the change of user interest. To solve the above problems, a target tracking item similarity incremental update mechanism suitable for online applications is proposed, so that the recommendation system can update the similarity data between the corresponding item and other items in real time after the current user submits the item score, so that the recommendation system can recommend based on the latest item similarity data, To adapt to changes in user interests.

The objective for this paper is to develop an adaptive image retrieval system with an innovative approach to use artificial neural networks and clustering techniques to retrieve images similar to the input image. This paper involves retrieving images from huge image databases which are visually similar to a query image. Due to the enormous increase in image database size, and its high usage in a variety of applications, need for the development of Content Based Image Retrieval arose. It summarizes the problem, the proposed solution, and the desired results. The system uses neural networks, relevance feedback and clustering. The system is made intelligent by making the system learn the user's preference as feedback. The relevance feedback is used to improve the precision of the system by analyzing user's relevance feedback for each retrieved image while neural network and clustering techniques are used to reduce the time complexity of the system. The system uses three-layered neural network to train the system using image clusters as training dataset by a supervised approach. Also after taking the feedback from the user, the image clusters are re-clustered by rearranging the images after a successful retrieval. Given a user query as an image, the neural system retrieves related images by computing similarities with images in the given image clusters. To provide preference, from all the retrieved images user selects an image as relevant one and all other are hence treated as irrelevant ones. So, the rank of the selected image is increased while the ranks of other images are decreased. With this feedback, the system refinement method estimates global approximations and adjusts the similarity probabilities.

This paper proposes a method for retrieving images containing a specific target person from a given image collection of group photos. This can be realized by query-by-example methods which compare the facial visual features of the target person in the given query image and of each person in the images in the image collection. However, since images are often taken under various conditions, facial appearance of the same person can vary. Since socially related people such as family and friends are often taken photos together, the people co-occurrence relations in the same images can also be a useful clue for image retrieval. Focusing on such people co-occurrence relations, we propose Bag of People (BoP) features which represent both the facial appearances of persons and their co-occurrence relations in the same images. By using the BoP features, a classifier for classifying images into two classes, images containing the target person and other images, can be trained from a small number of images labeled by user's relevance feedback. Furthermore, since the labeled images obtained by relevance feedback are much fewer than unlabeled images in the image collection, an active learning method is used to select useful images to train the classifier. When retrieving images of 24 persons in total from 550 images, after five feedback iterations, the mean average precision of 0.94 was obtained by considering the people co-occurrence relations, as against 0.69 when considering only the target person.

The increasing abundance of content on the web has made information filtering even more important in helping users find information related to their interests. Personalization of web search is one such effort, that aims at improving the efficiency with which a user finds results relevant to his query. This is done by keeping track of a user's individual interests, and taking it into account while returning search results. We propose a robust user modeling technique that implicitly creates a Dynamic Category Interest Tree (DCIT), using a general ontology of the web and a set of web pages collected over time that give an insight into a user's interests. The DCIT is designed to use a fuzzy classification technique to keep track of what topics a user is interested in, his amount of interest in a topic, as well as reflect his changing interests overtime. The DCIT consists of a general ontology of the web, where each node represents a topic and consists of keywords that are usually used to describe that topic or category. Additional keywords that the user frequently associates with a topic, such as names of important people, organizations, or a specialized terminology, etc. Are also incorporated into the relevant topic. We use the Apriori Algorithm to extract these associated words from the user's web history in order to more accurately define the user's categories of interest. The DCIT is initially created by a content based approach using only the browsing history of the user, and is later further enhanced through collaborative filtering using the k-nearest neighbour-based algorithm. We propose a technique to re-rank the results from a search engine according to their relevance to a user, based on his implicitly learned DCIT. According to experimental results, our DCIT based ranking often outperforms search engines such as Google when it comes to retrieving web pages that are more relevant to a user's interest.

Recent sequential recommendation models rely increasingly on consecutive short-term user-item interaction sequences to model user interests. These approaches have, however, raised concerns about both short- and long-term interests. (1) short-term: interaction sequences may not result from a monolithic interest, but rather from several intertwined interests, even within a short period of time, resulting in their failures to model skip behaviors; (2) long-term: interaction sequences are primarily observed sparsely at discrete intervals, other than consecutively over the long run. This renders difficulty in inferring long-term interests, since only discrete interest representations can be derived, without taking into account interest dynamics across sequences. In this study, we address these concerns by learning (1) multi-scale representations of short-term interests; and (2) dynamics-aware representations of long-term interests. To this end, we present an Interest Dynamics modeling framework using generative Neural Processes, coined IDNP, to model user interests from a functional perspective. IDNP learns a global interest function family to define each user's long-term interest as a function instantiation, manifesting interest dynamics through function continuity. Specifically, IDNP first encodes each user's short-term interactions into multi-scale representations, which are then summarized as user context. By combining latent global interest with user context, IDNP then reconstructs long-term user interest functions and predicts interactions at upcoming query timestep. Moreover, IDNP can model such interest functions even when interaction sequences are limited and non-consecutive. Extensive experiments on four real-world datasets demonstrate that our model outperforms the state-of-the-art on various evaluation metrics.

Content-based image retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. While these research efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these efforts have relatively ignored two distinct characteristics of CBIR systems: (1) the gap between high level concepts and low level features; (2) subjectivity of human perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high level query and perception subjectivity are captured by dynamically updated weights based on the user's relevance feedback. The experimental results show that the proposed approach greatly reduces the user's effort of composing a query and captures the user's information need more precisely.

The expressive power of logic is believed to be able to model most of the fundamental aspects of information retrieval (IR). However, it is also understood that classical logic is ineffective for handling partiality and uncertainty in IR. Applying non-classical logics such as the AGM belief revision logic and the possibilistic logic to adaptive information retrieval is appealing since they provide a powerful and rigorous framework to model partiality and uncertainty inherent in any IR processes. The maxi-adjustment method, which is an effective computational apparatus of the AGM paradigm, is applied to develop the learning components of the adaptive information agents. Essentially, maxi-adjustment allows the partial representation K of a user's information needs N to be refined gradually based on the user's relevance feedback t. Generally speaking, learning in adaptive information agents is characterised by the AGM belief revision Kt*. On the other hand, possibilistic logic supports a gradated assessment of the uncertainty arising from matching K with the imperfect characterisation d of an information object D. Information matching in adaptive information agents is underpinned by K ƕ d, where ƕ is the possibilistic inference relation. This paper illustrates how maxi-adjustment and possibilistic deduction can be applied to develop the adaptive information agents. Their impact on the agents learning autonomy and explanatory power is also discussed.

This paper proposes a new texture image retrieval scheme based on contourlet transform and support vector machines (SVMs). In the scheme, the energies and the generalized Gaussian distribution (GGD) parameters are used to represent the contourlet subband features. Using the representations, a two-run SVM retrieval algorithm which employs an one-class SVM followed by a two-class SVM is proposed to carry out the perceptual similarity measurement. For the query image, the one-class SVM is used to obtain the effective initial training set with positive and negative samples. Using these initial samples, the two-class SVM is applied to refine on the image classification subject to the user’s relevance feedback. Compared with existing texture image retrieval methods, the proposed retrieval scheme is demonstrated respectively to be effective on the VisTex database of 640 texture images and the Brodatz database of 1760 texture images. Experimental results have shown that the proposed retrieval scheme can attain 99.38% and 98.07% of the average rates respectively for the two databases.KeywordsContourlet transformgeneralized Gaussian distributionsupport vector machinerelevance feedbacktexture image retrieval