There is a growing need to explore attributed graphs such as social networks, expert networks, and biological networks. A well-known mechanism for non-technical users to explore such graphs is keyword search, which receives a set of query keywords and returns a connected subgraph that contains the keywords. However, existing approaches, such as methods based on shortest paths between nodes containing

Recommender system (RS) can produce personalized service to users by analyzing their historical information. User-based collaborative filtering (UBCF) approach is widely utilized in practical RSs because of its excellent performance. However, the traditional UBCF suffers from several inherent problems, such as data sparsity and new user cold start. In this paper, we propose a novel approach, namely

The evaluation of recommender systems is an area with unsolved questions at several levels. Choosing the appropriate evaluation metric is one of such important issues. Ranking accuracy is generally identified as a prerequisite for recommendation to be useful. Ranking metrics have been adapted for this purpose from the Information Retrieval field into the recommendation task. In this article, we undertake

Although computing similarity is one of the fundamental challenges of Information Access tasks, the notion of similarity in this context is not yet completely understood from a formal, axiomatic perspective. In this paper we show how axiomatic explanations of similarity from other fields (Tversky’s axioms from the point of view of cognitive sciences, and metric spaces from the point of view of algebra)

We provide a uniform, general, and complete formal account of evaluation metrics for ranking, classification, clustering, and other information access problems. We leverage concepts from measurement theory, such as scale types and permissible transformation functions, and we capture the nature of evaluation metrics in many tasks by two formal definitions, which lead to a distinction of two metric/tasks

Online reviews are broadly believed to reflect consumers’ opinions towards the reviewed items. In this work, we postulate that online reviews for experience goods also reflect something very different, the reviewer’s emotions while experiencing the item. We study the case of films, which are made with the intent of evoking an emotional response. We postulate that the emotions that the viewer experienced

Interactive NLP is a promising paradigm to close the gap between automatic NLP systems and the human upper bound. Preference-based interactive learning has been successfully applied, but the existing methods require several thousand interaction rounds even in simulations with perfect user feedback. In this paper, we study preference-based interactive summarisation. To reduce the number of interaction

Learning to rank (LtR) techniques leverage assessed samples of query-document relevance to learn effective ranking functions able to exploit the noisy signals hidden in the features used to represent queries and documents. In this paper we explore how to enhance the state-of-the-art LambdaMart LtR algorithm by integrating in the training process an explicit knowledge of the underlying user-interaction

A major challenge in cross-language information retrieval (CLIR) is the adoption of translation knowledge in retrieval models, as it affects term weighting which is known to highly impact the retrieval performance. Despite its importance, how different approaches for integration of translation knowledge into retrieval models relatively perform has not been analytically examined. In this paper, we present

We undertake a detailed examination of the steps that make up offline experiments for recommender system evaluation, including the manner in which the available ratings are filtered and split into training and test; the selection of a subset of the available users for the evaluation; the choice of strategy to handle the background effects that arise when the system is unable to provide scores for some

According to common relevance-judgments regimes, such as TREC’s, a document can be deemed relevant to a query even if it contains a very short passage of text with pertinent information. This fact has motivated work on passage-based document retrieval: document ranking methods that induce information from the document’s passages. However, the main source of passage-based information utilized was passage-query

This study investigates the impact of search query intention when evaluating and managing search engine advertising. Specifically, we study whether the performance of a search engine advertising campaign depends on the informational, transactional, and navigational search intentions and also consider the appearance of an organic result alongside a search engine advertisement on the same search engine

Human–computer conversation is an active research topic in natural language processing. One of the representative methods to build conversation systems uses the sequence-to-sequence (Seq2seq) model through neural networks. However, with limited input information, the Seq2seq model tends to generate meaningless and trivial responses. It can be greatly enhanced if more supplementary information is provided

Quantification is the task of estimating, given a set \(\sigma \) of unlabelled items and a set of classes \({\mathcal {C}}=\{c_{1}, \ldots , c_{|{\mathcal {C}}|}\}\), the prevalence (or “relative frequency”) in \(\sigma \) of each class \(c_{i}\in {\mathcal {C}}\). While quantification may in principle be solved by classifying each item in \(\sigma \) and counting how many such items have been labelled

Evaluation measures are the basis for quantifying the performance of IR systems and the way in which their values can be processed to perform statistical analyses depends on the scales on which these measures are defined. For example, mean and variance should be computed only when relying on interval scales. In our previous work we defined a theory of IR evaluation measures, based on the representational

This study uses a novel simulation framework to evaluate whether the time and effort necessary to achieve high recall using active learning is reduced by presenting the reviewer with isolated sentences, as opposed to full documents, for relevance feedback. Under the weak assumption that more time and effort is required to review an entire document than a single sentence, simulation results indicate

Online shopping has become more and more popular in recent years, which leads to a prosperity on online platforms. Generally, the identical products are provided by many sellers on multiple platforms. Thus the comparison between products on multiple platforms becomes a basic demand for both consumers and sellers. However, identifying identical products on multiple platforms is difficult because the

Query auto completion (QAC) is used in search interfaces to interactively offer a list of suggestions to users as they enter queries. The suggested completions are updated each time the user modifies their partial query, as they either add further keystrokes or interact directly with completions that have been offered. In this work we use a state model to capture the possible interactions that can

Result diversification aims to provide searchers with a broader view of a given topic while attempting to maximise the chances of retrieving relevant material. Diversifying results also aims to reduce search bias by increasing the coverage over different aspects of the topic. As such, searchers should learn more about the given topic in general. Despite diversification algorithms being introduced over

When evaluating IR run effectiveness using a test collection, a key question is: What search topics should be used? We explore what happens to measurement accuracy when the number of topics in a test collection is reduced, using the Million Query 2007, TeraByte 2006, and Robust 2004 TREC collections, which all feature more than 50 topics, something that has not been examined in past work. Our analysis

Search result diversification aims at covering different user intents by returning a diversified document list. Most existing diversity measures require a predefined set of intents for a given query, where it is assumed that there is no relationship across these intents. However, studies have shown that modeling a hierarchy of intents has some benefits over the standard measure of using a flat list

Although document filtering is simple to define, there is a wide range of different evaluation measures that have been proposed in the literature, all of which have been subject to criticism. Our goal is to compare metrics from a formal point of view, in order to understand whether each metric is appropriate, why and when, in order to achieve a better understanding of the similarities and differences

Online news media sites are emerging as the primary source of news for a large number of users. Due to a large number of stories being published in these media sites, users usually rely on news recommendation systems to find important news. In this work, we focus on automatically recommending news stories to all users of such media websites, where the selection is not influenced by a particular user’s

While today’s web search engines are designed for single-user search, over the years research efforts have shown that complex information needs—which are explorative, open-ended and multi-faceted—can be answered more efficiently and effectively when searching in collaboration. Collaborative search (and sensemaking) research has investigated techniques, algorithms and interface affordances to gain insights

In Web search, entity-seeking queries often trigger a special question answering (QA) system. It may use a parser to interpret the question to a structured query, execute that on a knowledge graph (KG), and return direct entity responses. QA systems based on precise parsing tend to be brittle: minor syntax variations may dramatically change the response. Moreover, KG coverage is patchy. At the other

The correlation between the demographics of users and the text they write has been investigated through literary texts and, more recently, social media. However, differences pertaining to language use in search engines has not been thoroughly analyzed, especially for age and gender differences. Such differences are important especially due to the growing use of search engine data in the study of human

Automated construction of knowledge hierarchies from huge data corpora is gaining increasing attention in recent years, in order to tackle the infeasibility of manually extracting and semantically linking millions of concepts. As a knowledge hierarchy evolves with these automated techniques, there is a need for measures to assess its temporal evolution, quantifying the similarities between different

A typical information retrieval (IR) system applies a single retrieval strategy to every information need of users. However, the results of the past IR experiments show that a particular retrieval strategy is in general good at fulfilling some type of information needs while failing to fulfil some other type, i.e., high variation in retrieval effectiveness across information needs. On the other hand

Today, the practice of returning entities from a knowledge base in response to search queries has become widespread. One of the distinctive characteristics of entities is that they are typed, i.e., assigned to some hierarchically organized type system (type taxonomy). The primary objective of this paper is to gain a better understanding of how entity type information can be utilized in entity retrieval

Consumer health search (CHS) is a challenging domain with vocabulary mismatch and considerable domain expertise hampering peoples’ ability to formulate effective queries. We posit that using knowledge bases for query reformulation may help alleviate this problem. How to exploit knowledge bases for effective CHS is nontrivial, involving a swathe of key choices and design decisions (many of which are

We propose Neural variational set expansion to extract actionable information from a noisy knowledge graph (KG) and propose a general approach for increasing the interpretability of recommendation systems. We demonstrate the usefulness of applying a variational autoencoder to the Entity set expansion task based on a realistic automatically generated KG.

Many questions cannot be answered simply; their answers must include numerous nuanced details and context. Complex Answer Retrieval (CAR) is the retrieval of answers to such questions. These questions can be constructed from a topic entity (e.g., ‘cheese’) and a facet (e.g., ‘health effects’). While topic matching has been thoroughly explored, we observe that some facets use general language that is

Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the

Social media posts are a great source for life summaries aggregating activities, events, interactions and thoughts of the last months or years. They can be used for personal reminiscence as well as for keeping track with developments in the lives of not-so-close friends. One of the core challenges of automatically creating such summaries is to decide which posts are memorable, i.e., should be considered

Text representations using neural word embeddings have proven effective in many NLP applications. Recent researches adapt the traditional word embedding models to learn vectors of multiword expressions (concepts/entities). However, these methods are limited to textual knowledge bases (e.g., Wikipedia). In this paper, we propose a novel and simple technique for integrating the knowledge about concepts

The accurate prediction of users’ future interests on social networks allows one to perform future planning by studying how users will react if certain topics emerge in the future. It can improve areas such as targeted advertising and the efficient delivery of services. Despite the importance of predicting user future interests on social networks, existing works mainly focus on identifying user current

Link prediction is a prominent research direction e.g., for inferring upcoming interactions to be used in recommender systems. Although this problem of predicting links between users has been extensively studied in the past, research investigating this issue simultaneously in multiplex networks is rather rare so far. This is the focus of this paper. We investigate the extent to which trading interactions

In addition to being in contact with friends, online social networks are commonly used as a source of information, suggestions and recommendations from members of the community. Whenever we accept a suggestion or perform any action because it was recommended by a “friend”, we are being influenced by him/her. For this reason, it is useful for users seeking for interesting information to identify and

Although social media platforms serve diverse purposes, from social and professional networking to photo sharing and blogging, people frequently use them to share the thoughts and opinions and most importantly, their interests (e.g., politics, economy, sports). Understanding the interests of social media users is key to many applications that need to characterize them to recommend some services and

Every information retrieval (IR) model embeds in its scoring function a form of term frequency (TF) quantification. The contribution of the term frequency is determined by the properties of the function of the chosen TF quantification, and by its TF normalization. The first defines how independent the occurrences of multiple terms are, while the second acts on mitigating the a priori probability of

The way in which people gather information about events and form their own opinion on them has changed dramatically with the advent of social media. For many readers, the news gathered from online sources has become an opportunity to share points of view and information within micro-blogging platforms such as Twitter, mainly aimed at satisfying their communication needs. Furthermore, the need to deepen

Recommender systems have dramatically changed the way we consume content. Internet applications rely on these systems to help users navigate among the ever-increasing number of choices available. However, most current systems ignore the fact that user preferences can change according to context, resulting in recommendations that do not fit user interests. This research addresses these issues by proposing

Since its inception in 2013, one of the key contributions of the CLEF eHealth evaluation campaign has been the organization of an ad-hoc information retrieval (IR) benchmarking task. This IR task evaluates systems intended to support laypeople searching for and understanding health information. Each year the task provides registered participants with standard IR test collections consisting of a document

We address the feature extraction problem for document ranking in information retrieval. We then propose LifeRank, a Linear feature extraction algorithm for Ranking. In LifeRank, we regard each document collection for ranking as a matrix, referred to as the original matrix. We try to optimize a transformation matrix, so that a new matrix (dataset) can be generated as the product of the original matrix

An information retrieval (IR) system can often fail to retrieve relevant documents due to the incomplete specification of information need in the user’s query. Pseudo-relevance feedback (PRF) aims to improve IR effectiveness by exploiting potentially relevant aspects of the information need present in the documents retrieved in an initial search. Standard PRF approaches utilize the information contained

The rapid growth of the Web has increased the difficulty of finding the information that can address the users’ information needs. A number of recommendation approaches have been developed to tackle this problem. The increase in the number of data providers has necessitated the development of multi-publisher recommender systems; systems that include more than one item/data provider. In such environments

Currently users on social media post their opinion and feelings about almost everything. This online behavior has led to numerous applications where social media data are used to measure public opinion in a similar way as a poll or a survey. In this paper, we will present an application of social media mining for the art market. To the best of our knowledge, this will be the first attempt to mine social

Query expansion (QE) is an important process in information retrieval applications that improves the user query and helps in retrieving relevant results. In this paper, we introduce a hybrid query expansion model (HQE) that investigates how external resources can be combined to association rules mining and used to enhance expansion terms generation and selection. The HQE model can be processed in different

This article introduces a new language-independent approach for creating a large-scale high-quality test collection of tweets that supports multiple information retrieval (IR) tasks without running a shared-task campaign. The adopted approach (demonstrated over Arabic tweets) designs the collection around significant (i.e., popular) events, which enables the development of topics that represent frequent

The need to cluster small text corpora composed of a few hundreds of short texts rises in various applications; e.g., clustering top-retrieved documents based on their snippets. This clustering task is challenging due to the vocabulary mismatch between short texts and the insufficient corpus-based statistics (e.g., term co-occurrence statistics) due to the corpus size. We address this clustering challenge

Duplicate content on the Web occurs within the same website or across multiple websites. The latter is mainly associated with the existence of website replicas—sites that are perceptibly similar. Replication may be accidental, intentional or malicious, but no matter the reason, search engines suffer greatly either from unnecessarily storing and moving duplicate data, or from providing search results

A recent “third wave” of neural network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, work in this area is often referred to as deep learning. Recent years have witnessed an explosive growth of research into

Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to

Operational multimodal information retrieval systems have to deal with increasingly complex document collections and queries that are composed of a large set of textual and non-textual modalities such as ratings, prices, timestamps, geographical coordinates, etc. The resulting combinatorial explosion of modality combinations makes it intractable to treat each modality individually and to obtain suitable

In this paper we tackle the problem of image search when the query is a short textual description of the image the user is looking for. We choose to implement the actual search process as a similarity search in a visual feature space, by learning to translate a textual query into a visual representation. Searching in the visual feature space has the advantage that any update to the translation model

Contextual factors greatly affect users’ preferences for music, so they can benefit music recommendation and music retrieval. However, how to acquire and utilize the contextual information is still facing challenges. This paper proposes a novel approach for context-aware music recommendation, which infers users’ preferences for music, and then recommends music pieces that fit their real-time requirements

Session search, the task of document retrieval for a series of queries in a session, has been receiving increasing attention from the information retrieval research community. Session search exhibits the properties of rich user-system interactions and temporal dependency. These properties lead to our proposal of using partially observable Markov decision process to model session search. On the basis

Automatic detection of source code plagiarism is an important research field for both the commercial software industry and within the research community. Existing methods of plagiarism detection primarily involve exhaustive pairwise document comparison, which does not scale well for large software collections. To achieve scalability, we approach the problem from an information retrieval (IR) perspective

A new method is proposed for clustering XML documents by structure-constrained phrases. It is implemented by three machine-learning approaches previously unexplored in the XML domain, namely non-negative matrix (tri-)factorization, co-clustering and automatic transactional clustering. A novel class of XML features approximately captures structure-constrained phrases as n-grams contextualized by root-to-leaf

There is an increasing consensus in the Recommender Systems community that the dominant error-based evaluation metrics are insufficient, and mostly inadequate, to properly assess the practical effectiveness of recommendations. Seeking to evaluate recommendation rankings—which largely determine the effective accuracy in matching user needs—rather than predicted rating values, Information Retrieval metrics