Data mining, machine learning, data science, recommender systems
Liu, Q, Kang, B, Yu, K, Qi, X, Li, J, Wang, S & Li, HA 2020, 'Contour-Maintaining-Based Image Adaption for an Efficient Ambulance Service in Intelligent Transportation Systems', IEEE Access, vol. 8, pp. 12644-12654.View/Download from: Publisher's site
© 2013 IEEE. Ambulance services play a vital role in intelligent transportation systems (ITS). In an intelligent ambulance system, the medical images can help doctors quickly and accurately understand the patients' condition during first aid. On various display devices in different kinds of ambulances, content-aware image adaption can be used to better present the medical image among different display resolutions and aspect ratios. Most existing methods mainly focus on visual protection of salient areas, such as specific organ parts of the human body, with less attention paid to the visual effect of unimportant areas. However, the human visual system is more sensitive to the edge and contour of images, which are important for ambulance services. To improve the visual effect of adapted images, a contour-maintaining-based image adaption method for an efficient ambulance service in ITS is proposed here. Firstly, the proposed method innovatively combines the weighted gradient, saliency, and edge maps into an importance map. Secondly, energy is optimized for reducing contour distortion and interruption according to the visual slope and curvature of contours and edges in non-salient areas. Finally, applying the sub-procedure of a forward seam carving method, the optimal seams can more evenly pass through the contour areas. The experimental results demonstrate that the proposed method is more effective than other similar methods.
IEEE Revealing complex relations between entities (e.g., items within or between transactions) is of great significance for business optimization, prediction, and decision making. Such relations include not only co-occurrence-based explicit relations but also nonco-occurrence-based implicit ones. Explicit relations have been substantially studied by rule mining-based approaches, including association rule mining and causal rule discovery. In contrast, implicit relations have received much less attention but could be more actionable. In this paper, we focus on the implicit relations between items which rarely or never co-occur while each of them co-occurs with other identical items (link items) with a high probability. A framework integrates both explicit and hidden item dependencies and a corresponding efficient algorithm IRRMiner captures such implicit relations with implicit rule inference. Experimental results show that IRRMiner not only infers implicit rules of various sizes consisting of both frequent and infrequent items effectively, it also runs at least four times faster than IARMiner, a typical indirect association rule mining algorithm which can only mine size-2 indirect association rules between frequent items. IRRMiner is applied to make recommendations and shows that the identified implicit rules can increase recommendation reliability.
Lu, W, Meng, F, Wang, S, Zhang, G, Zhang, X, Ouyang, A & Zhang, X 2019, 'Graph-based Chinese word sense disambiguation with multi-knowledge integration', Computers, Materials and Continua, vol. 61, no. 1, pp. 197-212.View/Download from: Publisher's site
© 2019 Tech Science Press. All rights reserved. Word sense disambiguation (WSD) is a fundamental but significant task in natural language processing, which directly affects the performance of upper applications. However, WSD is very challenging due to the problem of knowledge bottleneck, i.e., it is hard to acquire abundant disambiguation knowledge, especially in Chinese. To solve this problem, this paper proposes a graph-based Chinese WSD method with multi-knowledge integration. Particularly, a graph model combining various Chinese and English knowledge resources by word sense mapping is designed. Firstly, the content words in a Chinese ambiguous sentence are extracted and mapped to English words with BabelNet. Then, English word similarity is computed based on English word embeddings and knowledge base. Chinese word similarity is evaluated with Chinese word embedding and HowNet, respectively. The weights of the three kinds of word similarity are optimized with simulated annealing algorithm so as to obtain their overall similarities, which are utilized to construct a disambiguation graph. The graph scoring algorithm evaluates the importance of each word sense node and judge the right senses of the ambiguous words. Extensive experimental results on SemEval dataset show that our proposed WSD method significantly outperforms the baselines.
Wang, S, Hu, L, Wang, Y, Cao, L, Sheng, QZ & Orgun, M 2019, 'Sequential recommender systems: Challenges, progress and prospects', IJCAI International Joint Conference on Artificial Intelligence, pp. 6332-6338.
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. The emerging topic of sequential recommender systems (SRSs) has attracted increasing attention in recent years. Different from the conventional recommender systems (RSs) including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users' preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations. In this paper, we provide a systematic review on SRSs. We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic. Finally, we discuss the important research directions in this vibrant area.
Wang, S, Hu, L, Wang, Y, Sheng, QZ, Orgun, M & Cao, L 2019, 'Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks', IJCAI International Joint Conference on Artificial Intelligence, pp. 3771-3777.
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. A session-based recommender system (SBRS) suggests the next item by modeling the dependencies between items in a session. Most of existing SBRSs assume the items inside a session are associated with one (implicit) purpose. However, this may not always be true in reality, and a session may often consist of multiple subsets of items for different purposes (e.g., breakfast and decoration). Specifically, items (e.g., bread and milk) in a subset have strong purpose-specific dependencies whereas items (e.g., bread and vase) from different subsets have much weaker or even no dependencies due to the difference of purposes. Therefore, we propose a mixture-channel model to accommodate the multi-purpose item subsets for more precisely representing a session. To address the shortcomings in existing SBRSs, this model recommends more diverse items to satisfy different purposes. Accordingly, we design effective mixture-channel purpose routing networks (MCPRNs) with a purpose routing network to detect the purposes of each item and assign them into the corresponding channels. Moreover, a purpose-specific recurrent network is devised to model the dependencies between items within each channel for a specific purpose. The experimental results show the superiority of MCPRN over the state-of-the-art methods in terms of both recommendation accuracy and diversity.
Wang, S, Hu, L, Cao, L, Huang, X, Lian, D & Liu, W 2018, 'Attention-based transactional context embedding for next-item recommendation', Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, AAAI, New Orleans, United States, pp. 2532-2539.
To recommend the next item to a user in a transactional context is practical yet challenging in applications such as marketing campaigns. Transactional context refers to the items that are observable in a transaction. Most existing transaction-based recommender systems (TBRSs) make recommendations by mainly considering recently occurring items instead of all the ones observed in the current context. Moreover, they often assume a rigid order between items within a transaction, which is not always practical. More importantly, a long transaction often contains many items irreverent to the next choice, which tends to overwhelm the influence of a few truely relevant ones. Therefore, we posit that a good TBRS should not only consider all the observed items in the current transaction but also weight them with different relevance to build an attentive context that outputs the proper next item with a high probability. To this end, we design an effective attention-based transaction embedding model (ATEM) for context embedding to weight each observed item in a transaction without assuming order. The empirical study on real-world transaction datasets proves that ATEM significantly outperforms the state-of-the-art methods in terms of both accuracy and novelty.
Hu, L, Cao, L, Wang, S, Xu, G, Cao, J & Gu, Z 2017, 'Diversifying personalized recommendation with user-session context', IJCAI International Joint Conference on Artificial Intelligence, International Joint Conferences on Artifical Intelligence, International Joint Conferences on Artificial Intelligence Organization, Melbourne, Australia, pp. 1858-1864.
Recommender systems (RS) have become an integral part of our daily life. However, most current RS often repeatedly recommend items to users with similar profiles. We argue that recommendation should be diversified by leveraging session contexts with personalized user profiles. For this, current session-based RS (SBRS) often assume a rigidly ordered sequence over data which does not fit in many real-world cases. Moreover, personalization is often omitted in current SBRS. Accordingly, a personalized SBRS over relaxedly ordered user-session contexts is more pragmatic. In doing so, deep-structured models tend to be too complex to serve for online SBRS owing to the large number of users and items. Therefore, we design an efficient SBRS with shallow wide-in-wide-out networks, inspired by the successful experience in modern language modelings. The experiments on a real-world e-commerce dataset show the superiority of our model over the state-of-the-art methods.
Wang, S, Hu, L & Cao, L 2017, 'Perceiving the Next Choice with Comprehensive Transaction Embeddings for Online Recommendation', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer Link, Skopje, Macedonia, pp. 285-302.View/Download from: Publisher's site
© 2017, Springer International Publishing AG. To predict customer’s next choice in the context of what he/she has bought in a session is interesting and critical in the transaction domain especially for online shopping. Precise prediction leads to high quality recommendations and thus high benefit. Such kind of recommendation is usually formalized as transaction-based recommender systems (TBRS). Existing TBRS either tend to recommend popular items while ignore infrequent and newly-released ones (e.g., pattern-based RS) or assume a rigid order between items within a transaction (e.g., Markov Chain-based RS) which does not satisfy real-world cases in most time. In this paper, we propose a neural network-based comprehensive transaction embedding model (NTEM) which can effectively perceive the next choice in a transaction context. Specifically, we learn these comprehensive embeddings of both items and their features from relaxed ordered transactions. The relevance between items revealed by the transactions is encoded into such embeddings. With rich information embedded, such embeddings are powerful to predict the next choices given those already bought items. NTEM is a shallow wide-in-wide-out network, which is more efficient than deep networks considering large numbers of items and transactions. Experimental results on real-world datasets show that NTEM outperforms three typical TBRS models FPMC, PRME and GRU4Rec in terms of recommendation accuracy and novelty. Our implementation is available at https://github.com/shoujin88/NTEM-model.
Wang, S, Liu, W, Wu, J, Cao, L, Meng, Q & Kennedy, PJ 2016, 'Training deep neural networks on imbalanced data sets', Proceedings of the International Joint Conference on Neural Networks, IEEE International Joint Conference on Neural Networks, IEEE, Vancouver, Canada, pp. 4368-4374.View/Download from: Publisher's site
© 2016 IEEE.Deep learning has become increasingly popular in both academic and industrial areas in the past years. Various domains including pattern recognition, computer vision, and natural language processing have witnessed the great power of deep networks. However, current studies on deep learning mainly focus on data sets with balanced class labels, while its performance on imbalanced data is not well examined. Imbalanced data sets exist widely in real world and they have been providing great challenges for classification tasks. In this paper, we focus on the problem of classification using deep network on imbalanced data sets. Specifically, a novel loss function called mean false error together with its improved version mean squared false error are proposed for the training of deep networks on imbalanced data sets. The proposed method can effectively capture classification errors from both majority class and minority class equally. Experiments and comparisons demonstrate the superiority of the proposed approach compared with conventional methods in classifying imbalanced data sets on deep neural networks.