Solving the Cold-Start Problem in Recommender Systems with Social Tags

In this paper, based on the user-tag-object tripartite graphs, we propose a recommendation algorithm, which considers social tags as an important role for information retrieval. Besides its low cost of computational time, the experiment results of two real-world data sets, \emph{Del.icio.us} and \emph{MovieLens}, show it can enhance the algorithmic accuracy and diversity. Especially, it can obtain more personalized recommendation results when users have diverse topics of tags. In addition, the numerical results on the dependence of algorithmic accuracy indicates that the proposed algorithm is particularly effective for small degree objects, which reminds us of the well-known \emph{cold-start} problem in recommender systems. Further empirical study shows that the proposed algorithm can significantly solve this problem in social tagging systems with heterogeneous object degree distributions

Information Filtering on Coupled Social Networks

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm based on the coupled social networks, which considers the effects of both social influence and personalized preference. Experimental results on two real datasets, \emph{Epinions} and \emph{Friendfeed}, show that hybrid pattern can not only provide more accurate recommendations, but also can enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding structure and function of coupled social networks

Lasing oscillation condition and group delay control in gain-assisted plasmon-induced transparency

A gain-assisted plasmonic waveguide with two detuned resonators is investigated in the plasmon-induced transparency window. Phase map is employed to study power transmittance and group delay for varying gain coefficients and frequency detunings of the two resonators. The gain coefficient for lasing oscillation condition is analytically shown to vary quadratically with the frequency detuning. In the amplification regime below the lasing threshold, the spectrum implies not only large group delay, but also high transmittance and narrow linewidth. This is in contrast to those in the loss-compensation regime and the passive case in which there always exists a trade-off between the linewidth and the peak transmittance.Comment: 15 pages, 4 figure

Tag-Aware Recommender Systems: A State-of-the-Art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithm

Synthesis and Anticancer Activity of 4β-Triazole-podophyllotoxin Glycosides

AbstractThe objective of present study was to investigate the effect of various sounds on the green mustard’s (Brassica Juncea) morphology characteristic and productivity. The plant has been subjected to three various sound, namely classical music (rhythmic violin music), machine and traffic noise, and mixed sound (classical music and traffic noise) with 70-75 dB sound pressure level, from germination to harvest for three hours (7-10 am.) each day. Six parameters, i.e. germination, plant height, leaf width, leaf lenght, total plant lenght, and fresh weight, related with growth and productivity of plant were been monitored on regular basis.The results showed classical music improves germination up to 15% for 36 hours, plant height 13,5%, leaf width 14,8%, leaf length 14,2%, and wet weight 57,1%. In general, exposure to classical music gives thebest results on the morphological characteristics and productivity of green mustard.Keywords: Sound exposure, plant morphology , productivity, green mustardAbstrakPenelitian ini bertujuan untuk menginvestigasi efek paparan variasi suara terhadap karakteristik morfologi dan produktivitas tanaman sawi hijau. suara yang dipaparkan antara lain musik klasik (suara biola), bising lalu lintas dan mesin industri (noise) dan campuran antara musik klasik dan noise. Level suara yang digunakan berkisar antara 70-75 dB dimulai sejak masa perkecambahan hingga panen selama 3 jam tiap harinya dimulai pukul 07.00-10.00. Enam parameter yang diamati dan diambil datanya meliputi, daya berkecambah, tinggi tanaman, lebar daun, panjang daun, panjang tanaman total dan berat basah. Hasil penelitian menunjukkan bahwa musik klasik meningkatkan daya berkecambah sebesar 15%, tinggi tanaman sebesar 13,5%, lebar daun sebesar 14,8%, panjang daun sebesar 14,2%, dan berat basah sebesar 57,1%. Secara umum paparan musik klasik memberikan hasil terbaik terhadap karakteristik morfologi dan produktivitas sawi hijau.Kata kunci: Paparan suara, morfologi, produktivitas, sawi hijauDiterima: 21 Oktober 2013;Disetujui: 28 Januari 201

Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension

Multi-hop machine reading comprehension is a challenging task in natural language processing, which requires more reasoning ability and explainability. Spectral models based on graph convolutional networks grant the inferring abilities and lead to competitive results, however, part of them still face the challenge of analyzing the reasoning in a human-understandable way. Inspired by the concept of the Grandmother Cells in cognitive neuroscience, a spatial graph attention framework named crname, imitating the procedure was proposed. This model is designed to assemble the semantic features in multi-angle representations and automatically concentrate or alleviate the information for reasoning. The name "crname" is a metaphor for the pattern of the model: regard the subjects of queries as the start points of clues, take the reasoning entities as bridge points, and consider the latent candidate entities as the grandmother cells, and the clues end up in candidate entities. The proposed model allows us to visualize the reasoning graph and analyze the importance of edges connecting two entities and the selectivity in the mention and candidate nodes, which can be easier to be comprehended empirically. The official evaluations in open-domain multi-hop reading dataset WikiHop and Drug-drug Interactions dataset MedHop prove the validity of our approach and show the probability of the application of the model in the molecular biology domain

Collaborative filtering with diffusion-based similarity on tripartite graphs

Collaborative tags are playing more and more important role for the organization of information systems. In this paper, we study a personalized recommendation model making use of the ternary relations among users, objects and tags. We propose a measure of user similarity based on his preference and tagging information. Two kinds of similarities between users are calculated by using a diffusion-based process, which are then integrated for recommendation. We test the proposed method in a standard collaborative filtering framework with three metrics: ranking score, Recall and Precision, and demonstrate that it performs better than the commonly used cosine similarity.Comment: 8 pages, 4 figures, 1 tabl

Bridgeness: A Local Index on Edge Significance in Maintaining Global Connectivity

Edges in a network can be divided into two kinds according to their different roles: some enhance the locality like the ones inside a cluster while others contribute to the global connectivity like the ones connecting two clusters. A recent study by Onnela et al uncovered the weak ties effects in mobile communication. In this article, we provide complementary results on document networks, that is, the edges connecting less similar nodes in content are more significant in maintaining the global connectivity. We propose an index named bridgeness to quantify the edge significance in maintaining connectivity, which only depends on local information of network topology. We compare the bridgeness with content similarity and some other structural indices according to an edge percolation process. Experimental results on document networks show that the bridgeness outperforms content similarity in characterizing the edge significance. Furthermore, extensive numerical results on disparate networks indicate that the bridgeness is also better than some well-known indices on edge significance, including the Jaccard coefficient, degree product and betweenness centrality.Comment: 10 pages, 4 figures, 1 tabl