We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

The Long Tail: How Technology is turning mass markets into millions of niches. (p. 15). This passage from The Long Tail, pretty much sums it all up. The Long Tail by Chris Anderson is a good and worthwhile read for information scientists, computer scientists, ecommerce researchers, and others interested in all areas of Web research. The central premise of the book is that the combination of (1) the Pareto or Zipf distribution (i.e., power law probability distribution) that is characteristic of Web traffic and (2) the direct access to consumers via Web technology has opened up new business opportunities in the ''long tail''. Producers and advertisers no longer have to target ''the big hits'' at the head of the distribution. Instead, they can target the small, niche communities or even individuals in the tail of the distribution. The long tail is has been studied by Web researchers and has been noted in term usage on search engines, access times to servers, and popularity of Web sites. Andersen points out that the long tail also applies to products sold on the Web. He recounts that a sizeable percentage of Amazon sales come from books that only sell a few copies, a large number of songs from Rhapsody get downloaded only once in a month, and a significant number of movies from Netflix only get ordered occasionally. However, since the storage is in digital form for the songs and music (and Amazon out sources the storage of books) there is little additional inventory cost of these items. This phenomenon across all Web companies has led to a broadening of participation by both producers and consumers that would not have happened without the Web. The idea of the long tail is well known, of course. What Anderson has done is present it in an interesting manner and in a Web ecommerce setting. He applies it to Web businesses and then relates the multitude of other factors ongoing that permit the actual implementation of the long tail effect. Anderson also expands on prior work on the long tail by introducing an element of time, given the distribution a three dimensional effect. All in all, it is a nifty idea. The book is comprised of 14 chapters, plus an Introduction. Chapter 1 presents an overview of what the long tail is. Chapter 2 discusses the ''head'', which is the top of the tail where the …

The Long Tail: Why the Future of Business is Selling Less of More

In this paper, a novel text clustering method, improved krill herd algorithm with a hybrid function, called MMKHA, is proposed as an efficient clustering way to obtain promising and precise results in this domain. Krill herd is a new swarm-based optimization algorithm that imitates the behavior of a group of live krill. The potential of this algorithm is high because it performs better than other optimization methods; it balances the process of exploration and exploitation by complementing the strength of local nearby searching and global wide-range searching. Text clustering is the process of grouping significant amounts of text documents into coherent clusters in which documents in the same cluster are relevant. For the purpose of the experiments, six versions are thoroughly investigated to determine the best version for solving the text clustering. Eight benchmark text datasets are used for the evaluation process available at the Laboratory of Computational Intelligence (LABIC). Seven evaluation measures are utilized to validate the proposed algorithms, namely, ASDC, accuracy, precision, recall, F-measure, purity, and entropy. The proposed algorithms are compared with the other successful algorithms published in the literature. The results proved that the proposed improved krill herd algorithm with hybrid function achieved almost all the best results for all datasets in comparison with the other comparative algorithms.

Hybrid clustering analysis using improved krill herd algorithm

A new method is developed for recommender systemsThe recommender system is developed based on collaborative filteringScalability and sparsity issues in recommender systems are solvedMovieLens and Yahoo! Webscope R4 datasets are used for method evaluationThe method is effective in solving the sparsity and scalability problems in CF Improving the efficiency of methods has been a big challenge in recommender systems It has been also important to consider the trade-off between the accuracy and the computation time in recommending the items by the recommender systems as they need to produce the recommendations accurately and meanwhile in real-time In this regard, this research develops a new hybrid recommendation method based on Collaborative Filtering (CF) approaches Accordingly, in this research we solve two main drawbacks of recommender systems, sparsity and scalability, using dimensionality reduction and ontology techniques Then, we use ontology to improve the accuracy of recommendations in CF part In the CF part, we also use a dimensionality reduction technique, Singular Value Decomposition(SVD), to find the most similar items and users in each cluster of items and users which can significantly improve the scalability of the recommendation method We evaluate the method on two real-world datasets to show its effectiveness and compare the results with the results of methods in the literature The results showed that our method is effective in improving the sparsity and scalability problems in CF

A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques

Collaborative filtering (CF) is the most successful approach for personalized product or service recommendations Neighborhood based collaborative filtering is an important class of CF, which is simple, intuitive and efficient product recommender system widely used in commercial domain Typically, neighborhood-based CF uses a similarity measure for finding similar users to an active user or similar products on which she rated Traditional similarity measures utilize ratings of only co-rated items while computing similarity between a pair of users Therefore, these measures are not suitable in a sparse data In this paper, we propose a similarity measure for neighborhood based CF, which uses all ratings made by a pair of users Proposed measure finds importance of each pair of rated items by exploiting Bhattacharyya similarity To show effectiveness of the measure, we compared performances of neighborhood based CFs using state-of-the-art similarity measures with the proposed measured based CF Recommendation results on a set of real data show that proposed measure based CF outperforms existing measures based CFs in various evaluation metrics

A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data

We first analyze the shortages of the existing similarity measures in collaborative filtering.And second, we propose a new user similarity model to overcome these drawbacks.We compare the new model with many other similarity measures on two real data sets.Experiments show that the new model can reach better performance than many existing similarity measures. Collaborative filtering has become one of the most used approaches to provide personalized services for users. The key of this approach is to find similar users or items using user-item rating matrix so that the system can show recommendations for users. However, most approaches related to this approach are based on similarity algorithms, such as cosine, Pearson correlation coefficient, and mean squared difference. These methods are not much effective, especially in the cold user conditions. This paper presents a new user similarity model to improve the recommendation performance when only few ratings are available to calculate the similarities for each user. The model not only considers the local context information of user ratings, but also the global preference of user behavior. Experiments on three real data sets are implemented and compared with many state-of-the-art similarity measures. The results show the superiority of the new similarity model in recommended performance.

A new user similarity model to improve the accuracy of collaborative filtering

Link prediction plays an important role in understanding intrinsic evolving mechanisms of networks. With the belief that the likelihood of the existence of a link between two nodes is strongly related with their similarity, many methods have been proposed to calculate node similarity based on node attributes and/or topological structures. Among a large variety of methods that take into account paths connecting the target pair of nodes, most of which neglect the heterogeneity of those paths. Our hypothesis is that a path consisting of small-degree nodes provides a strong evidence of similarity between two ends, accordingly, we propose a so-called sig- nificant path index in this Letter to leverage intermediate nodes' degrees in similarity calculation. Empirical experiments on twelve disparate real networks demonstrate that the proposed index outperforms the mainstream link prediction baselines.

Predicting missing links via significant paths

Link prediction plays an important role in understanding the intrinsic evolving mechanisms of networks. With the belief that the likelihood of the existence of a link between two nodes is strongly related to their similarity, many methods have been proposed to calculate node similarity based on node attributes and/or topological structures. Among a large variety of methods that take into account paths connecting the target pair of nodes, most of them neglect the heterogeneity of those paths. Our hypothesis is that a path consisting of small-degree nodes provides a strong evidence of similarity between two ends, accordingly, we propose a so-called significant path index in this letter to leverage intermediate nodes' degrees in similarity calculation. Empirical experiments on twelve disparate real networks demonstrate that the proposed index outperforms the mainstream link prediction baselines.

http://iopscience.iop.org/article/10.1209/0295-5075/106/18008/pdf

https://iopscience.iop.org/article/10.1088/1367-2630/aaf25d/pdf

Heterogeneous behavioral adoption in multiplex networks

Personalized recommendation has attracted a surge of interdisciplinary research. Especially, similarity-based methods in applications of real recommendation systems have achieved great success. However, the computations of similarities are overestimated or underestimated, in particular because of the defective strategy of unidirectional similarity estimation. In this paper, we solve this drawback by leveraging mutual correction of forward and backward similarity estimations, and propose a new personalized recommendation index, i.e., corrected similarity based inference (CSI). Through extensive experiments on four benchmark datasets, the results show a greater improvement of CSI in comparison with these mainstream baselines. And a detailed analysis is presented to unveil and understand the origin of such difference between CSI and mainstream indices.

https://iopscience.iop.org/article/10.1088/1742-5468/2014/07/P07004/pdf

Xuzhen Zhu

Papers

A new user similarity model to improve the accuracy of collaborative filtering

Predicting missing links via significant paths

Predicting missing links via significant paths

Heterogeneous behavioral adoption in multiplex networks

Personalized recommendation with corrected similarity