Cold-start Items Research Articles

KRAN: Knowledge Refining Attention Network for Recommendation

Recommender algorithms combining knowledge graph and graph convolutional network are becoming more and more popular recently. Specifically, attributes describing the items to be recommended are often used as additional information. These attributes along with items are highly interconnected, intrinsically forming a Knowledge Graph (KG). These algorithms use KGs as an auxiliary data source to alleviate the negative impact of data sparsity. However, these graph convolutional network based algorithms do not distinguish the importance of different neighbors of entities in the KG, and according to Pareto’s principle, the important neighbors only account for a small proportion. These traditional algorithms can not fully mine the useful information in the KG. To fully release the power of KGs for building recommender systems, we propose in this article KRAN, a Knowledge Refining Attention Network, which can subtly capture the characteristics of the KG and thus boost recommendation performance. We first introduce a traditional attention mechanism into the KG processing, making the knowledge extraction more targeted, and then propose a refining mechanism to improve the traditional attention mechanism to extract the knowledge in the KG more effectively. More precisely, KRAN is designed to use our proposed knowledge-refining attention mechanism to aggregate and obtain the representations of the entities (both attributes and items) in the KG. Our knowledge-refining attention mechanism first measures the relevance between an entity and it’s neighbors in the KG by attention coefficients, and then further refines the attention coefficients using a “richer-get-richer” principle, in order to focus on highly relevant neighbors while eliminating less relevant neighbors for noise reduction. In addition, for the item cold start problem, we propose KRAN-CD, a variant of KRAN, which further incorporates pre-trained KG embeddings to handle cold start items. Experiments show that KRAN and KRAN-CD consistently outperform state-of-the-art baselines across different settings.

A Novel Overlapping Method to Alleviate the Cold-Start Problem in Recommendation Systems

Recommendation systems (RSs) are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The main objective of RSs is to tool up users with desired items that meet their preferences. A major problem in RSs is called: “cold-start”; it is a potential problem called so in computer-based information systems which comprises a degree of automated data modeling. Particularly, it concerns the issue in which the system cannot draw any inferences nor have it yet gathered sufficient information about users or items. Since RSs performance is substantially limited by cold-start users and cold-start items problems; this research study takes the route for a major aim to attenuate users’ cold-start problem. Still in the process of researching, sundry studies have been conducted to tackle this issue by using clustering techniques to group users according to their social relations, their ratings or both. However, a clustering technique disregards a variety of users’ tastes. In this case, the researcher has adopted the overlapping technique as a tool to deal with the clustering technique’s defects. The advantage of the overlapping technique excels over others by allowing users to belong to multi-clusters at the same time according to their behavior in the social network and ratings feedback. On that account, a novel overlapping method is presented and applied. This latter is executed by using the partitioning around medoids (PAM) algorithm to implement the clustering, which is achieved by means of exploiting social relations and confidence values. After acquiring users’ clusters, the average distances are computed in each cluster. Thereafter, a content comparison is made regarding the distances between every user and the computed distances of the clusters. If the comparison result is less than or equal to the average distance of a cluster, a new user is added to this cluster. The singular value decomposition plus (SVD[Formula: see text]) method is then applied to every cluster to compute predictions values. The outcome is calculated by computing the average of mean absolute error (MAE) and root mean square error (RMSE) for every cluster. The model is tested by two real world datasets: Ciao and FilmTrust. Ultimately, findings have exhibited a great deal of insights on how the proposed model outperformed a number of the state-of-the-art studies in terms of prediction accuracy.

Sequence-Based Explainable Hybrid Song Recommendation.

Despite advances in deep learning methods for song recommendation, most existing methods do not take advantage of the sequential nature of song content. In addition, there is a lack of methods that can explain their predictions using the content of recommended songs and only a few approaches can handle the item cold start problem. In this work, we propose a hybrid deep learning model that uses collaborative filtering (CF) and deep learning sequence models on the Musical Instrument Digital Interface (MIDI) content of songs to provide accurate recommendations, while also being able to generate a relevant, personalized explanation for each recommended song. Compared to state-of-the-art methods, our validation experiments showed that in addition to generating explainable recommendations, our model stood out among the top performers in terms of recommendation accuracy and the ability to handle the item cold start problem. Moreover, validation shows that our personalized explanations capture properties that are in accordance with the user’s preferences.

Cold-start Sequential Recommendation via Meta Learner

This paper explores meta-learning in sequential recommendation to alleviate the item cold-start problem. Sequential recommendation aims to capture user's dynamic preferences based on historical behavior sequences and acts as a key component of most online recommendation scenarios. However, most previous methods have trouble recommending cold-start items, which are prevalent in those scenarios. As there is generally no side information in sequential recommendation task, previous cold-start methods could not be applied when only user-item interactions are available. Thus, we propose a Meta-learning-based Cold-Start Sequential Recommendation Framework, namely Mecos, to mitigate the item cold-start problem in sequential recommendation. This task is non-trivial as it targets at an important problem in a novel and challenging context. Mecos effectively extracts user preference from limited interactions and learns to match the target cold-start item with the potential user. Besides, our framework can be painlessly integrated with neural network-based models. Extensive experiments conducted on three real-world datasets verify the superiority of Mecos, with the average improvement up to 99%, 91%, and 70% in HR@10 over state-of-the-art baseline methods.

Real Estate Recommendation Approach for Solving the Item Cold-Start Problem

The item cold-start problem occurs when a recommendation system cannot recommend new items owing to record deficiencies and new listing omissions. When searching for real estate, users can register a concurrent interest in recent and prior projects. Thus, an approach to recommend cold-start and warm-start items simultaneously must be determined. Furthermore, unrequired membership and stop-by behavior cause real estate recommendations to have many cold-start and new users. This characteristic encourages the use of a content-based approach and a session-based recommendation system. Herein, we propose a real estate recommendation approach for solving the item cold-start problem with acceptable warm-start item recommendations in the many-cold-start-users scenario. We modify a session-based recommendation system and employ existing mechanisms to efficiently deal with sequential and context information for the next-interacted item’s encoded attribute prediction. Subsequently, we use the nearest-neighbors approach using weighted cosine similarity to determine conforming candidates. We use Recall@K and MRR@K with the top-n recommendation to evaluate warm-start and cold-start item recommendations among different applied mechanisms and against the baselines. The results demonstrate the effectiveness of efficiently integrating the information and the difficulty in performing well in warm-start and cold-start item recommendations simultaneously. Our proposed approach illustrates the capability of solving the item cold-start problem while yielding promising results in both recommendations although neither result is the best. We believe that our approach provides a suitable compromise between both recommendations and that it will benefit recommendation tasks focusing on both recommendations.

Two-sided regularization model based on probabilistic matrix factorization and quantum similarity for recommender systems

Nowadays, with the advent of the age of Web 2.0, several social recommendation methods that use social network information have been proposed and achieved distinct developments. However, the most critical challenges for the existing majority of these methods are: (1) They tend to utilize only the available social relation between users and deal just with the cold-start user issue. (2) Besides, these methods are suffering from the lack of exploitation of content information such as social tagging, which can provide various sources to extract the item information to overcome the cold-start item and improve the recommendation quality. In this paper, we investigated the efficiency of data fusion by integrating multi-source of information. First, two essential factors, user-side information, and item-side information, are identified. Second, we developed a novel social recommendation model called Two-Sided Regularization (TSR), which is based on the probabilistic matrix factorization method. Finally, the effective quantum-based similarity method is adapted to measure the similarity between users and between items into the proposed model. Experimental results on the real dataset show that our proposed model TSR addresses both of cold-start user and item issues and outperforms state-of-the-art recommendation methods. These results indicate the importance of incorporating various sources of information in the recommendation process.

Deep Interest-Shifting Network with Meta-Embeddings for Fresh Item Recommendation

Nowadays, people have an increasing interest in fresh products such as new shoes and cosmetics. To this end, an E-commerce platform Taobao launched a fresh-item hub page on the recommender system, with which customers can freely and exclusively explore and purchase fresh items, namely, the New Tendency page. In this work, we make a first attempt to tackle the fresh-item recommendation task with two major challenges. First, a fresh-item recommendation scenario usually faces the challenge that the training data are highly deficient due to low page views. In this paper, we propose a deep interest-shifting network (DisNet), which transfers knowledge from a huge number of auxiliary data and then shifts user interests with contextual information. Furthermore, three interpretable interest-shifting operators are introduced. Second, since the items are fresh, many of them have never been exposed to users, leading to a severe cold-start problem. Though this problem can be alleviated by knowledge transfer, we further babysit these fully cold-start items by a relational meta-Id-embedding generator (RM-IdEG). Specifically, it trains the item id embeddings in a learning-to-learn manner and integrates relational information for better embedding performance. We conducted comprehensive experiments on both synthetic datasets as well as a real-world dataset. Both DisNet and RM-IdEG significantly outperform state-of-the-art approaches, respectively. Empirical results clearly verify the effectiveness of the proposed techniques, which are arguably promising and scalable in real-world applications.

Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines

AbstractOver the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue of the system and no data are available for that item. In content‐based RSs, the video items are typically represented by semantic attributes, when generating recommendations. These attributes require a group of experts or users for annotation, and still, the generated recommendations might not capture a complete picture of the users' preferences, for example, the visual tastes of users on video style. This article addresses this problem by proposing recommendation based on novel visual features that do not require human annotation and can represent visual aspects of video items. We have designed a novel evaluation methodology considering three realistic scenarios, that is, (a) extreme cold start, (b) moderate cold start and (c) warm‐start scenario. We have conducted a set of comprehensive experiments, and our results have shown the superior performance of recommendations based on visual features, in all of the evaluation scenarios.

A multistep priority-based ranking for top-N recommendation using social and tag information

Recommender system is a collection of information retrieval tools and techniques used for recommending items to users based on their choices. For improving recommendation accuracy, the use of extra information (e.g., social, trust, item tags, etc.) other than user-item rating data remains an active area of research since last one and half decade. In this paper, we propose a novel methodology for top-N item recommendation, which uses three different kinds of information: user-item rating data, social network among the users, and tags associated with the items. The proposed method has mainly five steps: (i) creation of neighbor users’ item set, (ii) construction of the user-feature matrix, (iii) computation of user priority, (iv) computation of item priority, and finally, (v) recommendation based on the item priority. We implement the proposed methodology with three recommendation dataset. We compare our results with that of the obtained from some state-of-the-art ranking methods and observe that recommendation accuracy is improved in the case of the proposed algorithm for both all users and cold-start users scenarios. The algorithm is also able to generate more cold-start items in the recommended item list.

Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations

Recommender systems are widely used in online platforms for easy exploration of personalized content. The best available recommendation algorithms are based on using the observed preference information among collaborating entities. A significant challenge in recommender system continues to be item cold-start recommendation: how to effectively recommend items with no observed or past preference information. Here we propose a two-stage algorithm based on soft clustering to provide an efficient solution to this problem. The crux of our approach lies in representing the items as soft-cluster embeddings in the space spanned by the side-information associated with the items. Though many item embedding approaches have been proposed for item cold-start recommendations in the past—and simple as they might appear—to the best of our knowledge, the approach based on soft-cluster embeddings has not been proposed in the research literature. Our experimental results on four benchmark datasets conclusively demonstrate that the proposed algorithm makes accurate recommendations in item cold-start settings compared to the state-of-the-art algorithms according to commonly used ranking metrics like Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP). The performance of our proposed algorithm on the MovieLens 20M dataset clearly demonstrates the scalability aspect of our algorithm compared to other popular algorithms. We also propose the metric Cold Items Precision (CIP) to quantify the ability of a system to recommend cold-start items. CIP can be used in conjunction with relevance ranking metrics like NDCG and MAP to measure the effectiveness of the cold-start recommendation algorithm.

Explicit feedback meet with implicit feedback in GPMF: a generalized probabilistic matrix factorization model for recommendation

Recommender Systems focus on implicit and explicit feedback or parameters of users for better rating prediction. Most of the existing recommender systems use only one type of feedback ignoring the other one. Based on the availability of resources, we may consider more number of feedback of both the types to predict user’s rating for a particular item more accurately. However to the best of our knowledge, there is no generalized model that is fitted for multiple parameters or feedback. In this paper, we have proposed a Generalized Probabilistic Matrix Factorization (GPMF) model which uses multiple parameters of both the types for recommendation. To build GPMF, first we develop three models focusing on users’ crucial side information. First model PMFE (P robabilistic M atrix F actorization with E xplicit_Feedback) is proposed based on an explicit feedback of users and second one is PMFI (P robabilistic M atrix F actorization with I mplicit_Feedback), where an implicit feedback is considered. The last one is PMFEI (P robabilistic M atrix F actorization with E xplicit and I mplicit_Feedback), where both explicit and implicit feedback are considered. Extensive experiments on real world datasets show that PMFEI performs better compare to baselines. PMFEI model also performs better compare to baselines for cold-start users and cold-start items also. In our experimental section, it is shown that GPMF performs better when we consider both explicit and implicit feedback. The effectiveness of each parameter is not same for recommendation. Using GPMF we can estimate the effectiveness of a parameter. Based on this effectiveness, we can add or remove more parameters for better rating prediction.

MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation

Sequential recommendation is a fundamental task for network applications, and it usually suffers from the item cold start problem due to the insufficiency of user feedbacks. There are currently three kinds of popular approaches which are respectively based on matrix factorization (MF) of collaborative filtering, Markov chain (MC), and recurrent neural network (RNN). Although widely used, they have some limitations. MF based methods could not capture dynamic user's interest. The strong Markov assumption greatly limits the performance of MC based methods. RNN based methods are still in the early stage of incorporating additional information. Based on these basic models, many methods with additional information only validate incorporating one modality in a separate way. In this work, to make the sequential recommendation and deal with the item cold start problem, we propose a Multi-View Recurrent Neural Network (MV-RNN}) model. Given the latent feature, MV-RNN can alleviate the item cold start problem by incorporating visual and textual information. First, At the input of MV-RNN, three different combinations of multi-view features are studied, like concatenation, fusion by addition and fusion by reconstructing the original multi-modal data. MV-RNN applies the recurrent structure to dynamically capture the user's interest. Second, we design a separate structure and a united structure on the hidden state of MV-RNN to explore a more effective way to handle multi-view features. Experiments on two real-world datasets show that MV-RNN can effectively generate the personalized ranking list, tackle the missing modalities problem and significantly alleviate the item cold start problem.

SCOL: similarity and credibility-based approach for opinion leaders detection in collaborative filtering-based recommender systems

Recommender systems (RSs) have recently gained significant attention from both research and industrial communities. These systems generate the recommendations of items in one of two ways, namely collaborative or content-based filtering. Collaborative filtering is a technique used by recommender systems in order to suggest to the user a set of items based on the opinions of other users who share with him the same preferences. One of the key issues in collaborative filtering systems (CFSs) is how to generate adequate recommendations for newcomers who rate only a small number of items, a problem known as cold start user. Another interesting problem is the cold start item when a new item is introduced in the system and cannot be recommended. In this paper, we present a clustering-based approach SCOL that aims to alleviate the cold start challenges; by identifying the most effective opinion leaders among the social network of the CFS. SCOL clustering focuses on the credibility and correlation similarity concepts.

SCOL: similarity and credibility-based approach for opinion leaders detection in collaborative filtering-based recommender systems

Recommender systems (RSs) have recently gained significant attention from both research and industrial communities. These systems generate the recommendations of items in one of two ways, namely collaborative or content-based filtering. Collaborative filtering is a technique used by recommender systems in order to suggest to the user a set of items based on the opinions of other users who share with him the same preferences. One of the key issues in collaborative filtering systems (CFSs) is how to generate adequate recommendations for newcomers who rate only a small number of items, a problem known as cold start user. Another interesting problem is the cold start item when a new item is introduced in the system and cannot be recommended. In this paper, we present a clustering-based approach SCOL that aims to alleviate the cold start challenges; by identifying the most effective opinion leaders among the social network of the CFS. SCOL clustering focuses on the credibility and correlation similarity concepts.

Deep Pairwise Hashing for Cold-start Recommendation

Recommendation efficiency and data sparsity problems have been regarded as two main challenges of real-world recommendation systems. Most existing works focus on improving recommendation accuracy instead of efficiency. In this paper, we propose a Deep Pairwise Hashing (DPH) to map users and items to binary vectors in the Hamming space, where a user's preference for an item can be efficiently calculated by the Hamming distance, which significantly improves the efficiency of online recommendation. To alleviate data sparsity and cold-start problems, the item content information exploited and integrated to learn effective representations of items. Specifically, we first pre-train robust item representation from item content data by a robust Denoising Auto-encoder instead of other deterministic deep learning frameworks. Then we fine-tune the entire recommender framework by adding a pairwise loss function with discrete constraints, which is more consistent with the ultimate goal of producing a ranked list of items. Finally, we adopt the alternating optimization method to optimize the proposed model with discrete constraints. Extensive experiments conducted on three different datasets show that DPH can significantly advance the state-of-the-art frameworks regarding data sparsity and cold-start item recommendation.

Multi-criteria based Item Recommendation Methods

This paper comprehensively investigates and compares the performance of various multi-criteria based item recommendation methods. The development of the methods consists of three main phases: predicting rating per criterion; aggregating rating prediction of all criteria; and generating the top- item recommendations. The multi-criteria based item recommendation methods are varied and labelled based on what approach is implemented to predict the rating per criterion, i.e., Collaborative Filtering (CF), Content-based (CB), and Hybrid. For the experiments, we generate two variations of datasets to represent the normal and cold-start conditions on the multi-criteria item recommendation system. The empirical analysis suggests that Hybrid and CF are best implemented on the normal and cold-start item conditions, respectively. On the other hand, CB should never be (solely) implemented in a multi-criteria based item recommendation system on any conditions.

Session Recommendation via Recurrent Neural Networks over Fisher Embedding Vectors.

Recommendation services bear great importance in e-commerce, shopping, tourism, and social media, as they aid the user in navigating through the items that are most relevant to their needs. In order to build recommender systems, organizations log the item consumption in their user sessions by using different sensors. For instance, Web sites use Web data loggers, museums and shopping centers rely on user in-door positioning systems to register user movement, and Location-Based Social Networks use Global Positioning System for out-door user tracking. Most organizations do not have a detailed history of previous activities or purchases by the user. Hence, in most cases recommenders propose items that are similar to the most recent ones viewed in the current user session. The corresponding task is called session based, and when only the last item is considered, it is referred to as item-to-item recommendation. A natural way of building next-item recommendations relies on item-to-item similarities and item-to-item transitions in the form of “people who viewed this, also viewed” lists. Such methods, however, depend on local information for the given item pairs, which can result in unstable results for items with short transaction history, especially in connection with the cold-start items that recently appeared and had no time yet to accumulate a sufficient number of transactions. In this paper, we give new algorithms by defining a global probabilistic similarity model of all the items based on Random Fields. We give a generative model for the item interactions based on arbitrary distance measures over the items, including explicit, implicit ratings and external metadata to estimate and predict item-to-item transition probabilities. We exploit our new model in two different item similarity algorithms, as well as a feature representation in a recurrent neural network based recommender. Our experiments on various publicly available data sets show that our new model outperforms simple similarity baseline methods and combines well with recent item-to-item and deep learning recommenders under several different performance metrics.

CrowdStart: Warming up cold-start items using crowdsourcing

The cold-start problem is one of the critical challenges in personalized recommender systems. A lot of existing work has been studied to exploit a user-item rating matrix as well as additional information for users/items, e.g., user profiles, item contents, and social relationships among users. However, because existing work is primarily biased to the auxiliary information for users/items, it is difficult to identify various and reliable item neighbors that are relevant to cold-start items. To alleviate this limitation, we propose a new crowd-enabled framework, called CrowdStart, which is an integrated human-machine approach for new item recommendation. The main contributions of the CrowdStart framework are two-fold: (1) To find various and reliable item neighbors for new items, we design two-step crowdsourcing tasks that harness not only machine-only algorithms but also the knowledge of crowd workers (including a few experts and a large number of non-expert workers in a crowdsourcing platform). (2) We develop a novel hybrid model to exploit the user-item rating matrix, the content information about items, and the crowd-based item neighbors from human knowledge into new item recommendation. To evaluate the effectiveness of the CrowdStart framework, we conduct extensive experiments including both a user study and simulation tests. Through the empirical study, we found that the CrowdStart framework provides relevant, diverse, reliable, and explainable crowd-based neighbors for new items and the crowd-based neighbors are meaningful for improving the accuracy of new item recommendation. The datasets and detailed experimental results are available at https://goo.gl/1iXTUE.

An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem

Item-based collaborative filtering is one of the most popular techniques in the recommender system to retrieve useful items for the users by finding the correlation among the items. Traditional item-based collaborative filtering works well when there exists sufficient rating data but cannot calculate similarity for new items, known as a cold-start problem. Usually, for the lack of rating data, the identification of the similarity among the cold-start items is difficult. As a result, existing techniques fail to predict accurate recommendations for cold-start items which also affects the recommender system’s performance. In this paper, two item-based similarity measures have been designed to overcome this problem by incorporating items’ genre data. An item might be uniform to other items as they might belong to more than one common genre. Thus, one of the similarity measures is defined by determining the degree of direct asymmetric correlation between items by considering their association of common genres. However, the similarity is determined between a couple of items where one of the items could be cold-start and another could be any highly rated item. Thus, the proposed similarity measure is accounted for as asymmetric by taking consideration of the item’s rating data. Another similarity measure is defined as the relative interconnection between items based on transitive inference. In addition, an enhanced prediction algorithm has been proposed so that it can calculate a better prediction for the recommendation. The proposed approach has experimented with two popular datasets that is Movielens and MovieTweets. In addition, it is found that the proposed technique performs better in comparison with the traditional techniques in a collaborative filtering recommender system. The proposed approach improved prediction accuracy for Movielens and MovieTweets approximately in terms of 3.42% & 8.58% mean absolute error, 7.25% & 3.29% precision, 7.20% & 7.55% recall, 8.76% & 5.15% f-measure and 49.3% and 16.49% mean reciprocal rank, respectively.

Session-Based Recommender System for Sustainable Digital Marketing

Many companies operate e-commerce websites to sell fashion products. Some customers want to buy products with intention of sustainability and therefore the companies need to suggest appropriate fashion products to those customers. Recommender systems are key applications in these sustainable digital marketing strategies and high performance is the most necessary factor. This research aims to improve recommendation systems’ performance by considering item session and attribute session information. We suggest the Item Session-Based Recommender (ISBR) and the Attribute Session-Based Recommenders (ASBRs) that use item and attribute session data independently, and then we suggest the Feature-Weighted Session-Based Recommenders (FWSBRs) that combine multiple ASBRs with various feature weighting schemes. Our experimental results show that FWSBR with chi-square feature weighting scheme outperforms ISBR, ASBRs, and Collaborative Filtering Recommender (CFR). In addition, it is notable that FWSBRs overcome the cold-start item problem, one significant limitation of CFR and ISBR, without losing performance.