A Survey on Context Learning <s> Explicit Analysis + Implicit Analysis <s> This paper presents a statistical approach to collaborative filtering and investigates the use of latent class models for predicting individual choices and preferences based on observed preference behavior. Two models are discussed and compared: the aspect model, a probabilistic latent space model which models individual preferences as a convex combination of preference factors, and the two-sided clustering model, which simultaneously partitions persons and objects into clusters. We present EM algorithms for different variants of the aspect model and derive an approximate EM algorithm based on a variational principle for the two-sided clustering model. The benefits of the different models are experimentally investigated on a large movie data set. <s> BIB001 </s> A Survey on Context Learning <s> Explicit Analysis + Implicit Analysis <s> Topic modeling has been a key problem for document analysis. One of the canonical approaches for topic modeling is Probabilistic Latent Semantic Indexing, which maximizes the joint probability of documents and terms in the corpus. The major disadvantage of PLSI is that it estimates the probability distribution of each document on the hidden topics independently and the number of parameters in the model grows linearly with the size of the corpus, which leads to serious problems with overfitting. Latent Dirichlet Allocation (LDA) is proposed to overcome this problem by treating the probability distribution of each document over topics as a hidden random variable. Both of these two methods discover the hidden topics in the Euclidean space. However, there is no convincing evidence that the document space is Euclidean, or flat. Therefore, it is more natural and reasonable to assume that the document space is a manifold, either linear or nonlinear. In this paper, we consider the problem of topic modeling on intrinsic document manifold. Specifically, we propose a novel algorithm called Laplacian Probabilistic Latent Semantic Indexing (LapPLSI) for topic modeling. LapPLSI models the document space as a submanifold embedded in the ambient space and directly performs the topic modeling on this document manifold in question. We compare the proposed LapPLSI approach with PLSI and LDA on three text data sets. Experimental results show that LapPLSI provides better representation in the sense of semantic structure. <s> BIB002 </s> A Survey on Context Learning <s> Explicit Analysis + Implicit Analysis <s> Evaluation measures act as objective functions to be optimized by information retrieval systems. Such objective functions must accurately reflect user requirements, particularly when tuning IR systems and learning ranking functions. Ambiguity in queries and redundancy in retrieved documents are poorly reflected by current evaluation measures. In this paper, we present a framework for evaluation that systematically rewards novelty and diversity. We develop this framework into a specific evaluation measure, based on cumulative gain. We demonstrate the feasibility of our approach using a test collection based on the TREC question answering track. <s> BIB003 </s> A Survey on Context Learning <s> Explicit Analysis + Implicit Analysis <s> Result diversification deals with ambiguous or multi-faceted queries by providing documents that cover as many subtopics of a query as possible. Various approaches to subtopic modeling have been proposed. Subtopics have been extracted internally, e.g., from retrieved documents, and externally, e.g., from Web resources such as query logs. Internally modeled subtopics are often implicitly represented, e.g., as latent topics, while externally modeled subtopics are often explicitly represented, e.g., as reformulated queries. We propose a framework that: i)combines both implicitly and explicitly represented subtopics; and ii)allows flexible combination of multiple external resources in a transparent and unified manner. Specifically, we use a random walk based approach to estimate the similarities of the explicit subtopics mined from a number of heterogeneous resources: click logs, anchor text, and web n-grams. We then use these similarities to regularize the latent topics extracted from the top-ranked documents, i.e., the internal (implicit) subtopics. Empirical results show that regularization with explicit subtopics extracted from the right resource leads to improved diversification results, indicating that the proposed regularization with (explicit) external resources forms better (implicit) topic models. Click logs and anchor text are shown to be more effective resources than web n-grams under current experimental settings. Combining resources does not always lead to better results, but achieves a robust performance. This robustness is important for two reasons: it cannot be predicted which resources will be most effective for a given query, and it is not yet known how to reliably determine the optimal model parameters for building implicit topic models. <s> BIB004 </s> A Survey on Context Learning <s> Explicit Analysis + Implicit Analysis <s> Groups play an essential role in many social websites which promote users' interactions and accelerate the diffusion of information. Recommending groups that users are really interested to join is significant for both users and social media. While traditional group recommendation problem has been extensively studied, we focus on a new type of the problem, i.e., event-based group recommendation. Unlike the other forms of groups, users join this type of groups mainly for participating offline events organized by group members or inviting other users to attend events sponsored by them. These characteristics determine that previously proposed approaches for group recommendation cannot be adapted to the new problem easily as they ignore the geographical influence and other explicit features of groups and users. In this paper, we propose a method called Pairwise Tag enhAnced and featuRe-based Matrix factorIzation for Group recommendAtioN (PTARMIGAN), which considers location features, social features, and implicit patterns simultaneously in a unified model. More specifically, we exploit matrix factorization to model interactions between users and groups. Meanwhile, we incorporate their profile information into pairwise enhanced latent factors respectively. We also utilize the linear model to capture explicit features. Due to the reinforcement between explicit features and implicit patterns, our approach can provide better group recommendations. We conducted a comprehensive performance evaluation on real word data sets and the experimental results demonstrate the effectiveness of our method. <s> BIB005 </s> A Survey on Context Learning <s> Explicit Analysis + Implicit Analysis <s> Title matching refers roughly to the following problem. We are given two strings of text obtained from different data sources. The texts refer to some underlying physical entities and the problem is to report whether the two strings refer to the same physical entity or not. There are manifestations of this problem in a variety of domains, such as product or bibliography matching, and location or person disambiguation. ::: ::: We propose a new approach to solving this problem, consisting of two main components. The first component uses Web searches to "enrich" the given pair of titles: making titles that refer to the same physical entity more similar, and those which do not, much less similar. A notion of similarity is then measured using the second component, where the tokens from the two titles are modelled as vertices of a "social" network graph. A "strength of ties" style of clustering algorithm is then applied on this to see whether they form one cohesive "community" (matching titles), or separately clustered communities (mismatching titles). Experimental results confirm the effectiveness of our approach over existing title matching methods across several input domains. <s> BIB006
While both explicit analysis and implicit analysis have shown their effectiveness in learning context information, both methods also have their limitations. We will introduce some composite models aiming to take advantage of both explicit analysis and latent analysis by combining them. Ref.  proposes a generative probabilistic recommender system which combines collaborative and content-based recommendations. The model extends the aspect model BIB001 to incorporate three-way co-occurrence among users, items and item content. Given an observation ðu; d; wÞ which denotes an event of user u accessing document d containing word w, when the latent topic is z, the probability distribution would be pðzÞpðujzÞpðdjzÞpðwjzÞ. We can obtain the joint distribution for the observation by marginalizing out latent topic z: pðu; d; wÞ ¼ P z pðzÞpðujzÞpðdjzÞpðwjzÞ. Hence, given a training set, the log likelihood L of the whole training set is L ¼  where nðu; d; wÞ is the number of times that observation ðu; d; wÞ has occurred in the dataset. The empirical results show that this proposed composite model is 39 percent better than the aspect model which considers only latent class variables with respect to predictive accuracy. Ref. BIB005 introduces another composite model for recommendation systems. The main idea of this paper is to recommend groups to social network users based on offline events. First the explicit features (location features and social features) are captured by a linear model:r l ðu; gÞ ¼ w T x, wherer l ðu; gÞ denotes the rating prediction between user u and group g based on the linear model, and x is the explicit feature vector. Then the latent features (the interactions between groups and users) are captured by matrix factorization: r m ðu; gÞ ¼ P T u P g , where P u and P g are the low-dimension latent factors for user u and group g. Moreover, the group bias and pairwise tags are incorporated into the model. They conduct experiments on the users of the social network Meetup in New York City and Los Angeles, and the composite model can achieve an 8 percent improvement in terms of AUC compared with the baseline methods which consider only either explicit or latent factors. In BIB004 , the authors propose a composite model which combines explicit and implicit topic representations for result diversification. As queries in web search are often short and underspecified, a diversified search result would increase users' average satisfaction by providing documents that cover as many subtopics of the query as possible. The model first uses a random walk based approach to compute the similarities between subtopics explicitly extracted from multiple web sources, and then regularize latent topic models with these similarities. In order to combine explicit and implicit subtopics, the model adopts the Laplacian PLSA BIB002 and uses the explicit similarities as the smoothing regularizer to train the latent topic model. Empirical results show that this composite model leads to more robust diversification results and outperforms the baseline by 14 percent in terms of a-nDCG@20 BIB003 . Ref. BIB006 introduces a composite model solution to the named entity disambiguation problem in e-commerce domain. The problem to address is that given two strings of text obtained from multiple data sources, such that each string corresponding to an underlying physical entity, the title matching problem is to report whether they refer to the same entity or not. The proposed model mainly consists of two components. The first component called Cross-Title Enrichment (CTE) aims to magnify the similarity or dissimilarity of a given pair of titles with the help of Web searches. The motivation for this component is similar with "query rewriting". This component refers to the explicit means of analysis. Specifically, by adding or deleting tokens to the given titles T 1 and T 2 , they are enriched with the help of Web searches such that the enriched titles still refer to the same physical object as the original titles, and yet make the title matching task easier. The second component called Community Detection for Approximate Matching (CDAM) computes the distance between the enriched titles based on a "social" like network constructed by the tokens and evaluates the number of latent communities in the network. If there is only a single close-knit community detected, then the two titles are deemed a match. The authors propose a new custom designed community detection algorithm to suit the needs of the domain as the conventional methods do not adapt well. Fig. 10 shows the network like structure between the tokens of two titles where an edge represents an implicit relationship between the tokens identified through web results. Thus, CDAM calculates the number of latent communities based on the inter-title edges and the nodes as max ðfðgðaÞ; gðbÞ; wða; bÞÞ À Þ a 2 T 1 nT 2 ; b 2 T 2 nT 1 ; 9Eða; bÞ  where gðaÞ is the difference between the average weight of outgoing edges of node a and the average weight of incoming edges of a (if the indegree of node a is 0, the the average weight of incoming edges is set to À1). fðÁÞ can be any aggregating function such as addition, wða; bÞ is the edge weight between node a and b, and is the penalty for isolated nodes. While CTE represents the explicit aspect of the approach to magnify the similarity or the dissimilarity, CDAM represents the implicit approach in calculating the actual similarity between the titles. The empirical results show that this model outperforms several state-of-the-art techniques and even many of the supervised methods.