Notification recommendation systems are critical to driving user engagement on professional platforms like LinkedIn. Designing such systems involves integrating heterogeneous signals across domains, capturing temporal dynamics, and optimizing for multiple, often competing, objectives. Graph Neural Networks (GNNs) provide a powerful framework for modeling complex interactions in such environments.

Query reformulation modifies the original query with the aim of better matching the vocabulary of the relevant documents, and consequently improving ranking effectiveness. Previous models typically generate words and phrases related to the original query, but do not consider how these words and phrases would fit together in actual queries. In this article, a novel framework is proposed that models reformulation as a distribution of actual queries, where each query is a variation of the original query. This approach considers an actual query as the basic unit and thus captures important query-level dependencies between words and phrases. An implementation of this framework that only uses publicly available resources is proposed, which makes fair comparisons with other methods using TREC collections possible. Specifically, this implementation consists of a query generation step that analyzes the passages containing query words to generate reformulated queries and a probability estimation step that learns a distribution for reformulated queries by optimizing the retrieval performance. Experiments on TREC collections show that the proposed model can significantly outperform previous reformulation models.

Search queries have evolved beyond keyword queries. Many complex queries such as verbose queries, natural language question queries and document-based queries are widely used in a variety of applications. Processing these complex queries usually requires a series of query operations, which results in multiple sequences of reformulated queries. However, previous query representations, either the "bag of words" method or the recently proposed "query distribution" method, cannot effectively model these query sequences, since they ignore the relationships between two queries. In this paper, a reformulation tree framework is proposed to organize multiple sequences of reformulated queries as a tree structure, where each path of the tree corresponds to a sequence of reformulated queries. Specifically, a two-level reformulation tree is implemented for verbose queries. This tree effectively combines two query operations, i.e., subset selection and query substitution, within the same framework. Furthermore, a weight estimation approach is proposed to assign weights to each node of the reformulation tree by considering the relationships with other nodes and directly optimizing retrieval performance. Experiments on TREC collections show that this reformulation tree based representation significantly outperforms the state-of-the-art techniques.

When the information need is not clear from the user query, a good strategy would be to return documents that cover as many aspects of the query as possible. To do this, the possible aspects of the query need to be automatically identified. In this paper, we propose to do this by clustering reformulated queries generated from publicly available resources and using each cluster to represent an aspect of the query. Our results show that the automatically generated reformulations for the TREC Web Track queries match up quite well with actual sub-topics of these queries identified by TREC experts. Moreover, agglomerative clustering using query-to-query similarity based on co-occurrence in text passages can provide clusters of high quality that potentially can be used to identify aspects.

Named entities are observed in a large portion of web search queries (named entity queries), where each entity can be associated with many different query terms that refer to various aspects of this entity. Organizing these query terms into topics helps understand major search intents about entities and the discovered topics are useful for applications such as query suggestion. Furthermore, we notice that named entities can often be organized into categories and those from the same category share many generic topics. Therefore, working on a category of named entities instead of individual ones helps avoid the problems caused by the sparsity and noise in the data. In this paper, Named Entity Topic Model (NETM) is proposed to discover generic topics for a category of named entities, where the quality of the generic topics is improved through the model design and the parameter initialization. Experiments based on query log data show that NETM discovers high-quality topics and outperforms the state-of-the-art techniques by 12.8% based on F1 measure.

Improving verbose (or long) queries poses a new challenge for search systems. Previous techniques mainly focused on two aspects, weighting the important words or phrases and selecting the best subset query. The former does not consider how words and phrases are used in actual subset queries, while the latter ignores alternative subset queries. Recently, a novel reformulation framework has been proposed to transform the original query as a distribution of reformulated queries, which overcomes the disadvantages of previous techniques. In this paper, we apply this framework to verbose queries, where a reformulated query is specified as a subset query. Experiments on TREC collections show that the query distribution based framework outperforms the state-of-the-art techniques.

Query reformulation modifies the original query with the aim of better matching the vocabulary of the relevant documents, and consequently improving ranking effectiveness. Previous techniques typically generate words and phrases related to the original query, but do not consider how these words and phrases would fit together in new queries. In this paper, we focus on an implementation of an approach that models reformulation as a distribution of queries, where each query is a variation of the original query. This approach considers a query as a basic unit and can capture important dependencies between words and phrases in the query. The implementation discussed here is based on passage analysis of the target corpus. Experiments on the TREC collection show that the proposed model for query reformulation significantly outperforms state-of-the-art methods.

Dealing with verbose (or long) queries poses a new challenge for information retrieval. Selecting a subset of the original query (a "sub-query") has been shown to be an effective method for improving these queries. In this paper, the distribution of sub-queries ("subset distribution") is formally modeled within a well-grounded framework. Specifically, sub-query selection is considered as a sequential labeling problem, where each query word in a verbose query is assigned a label of "keep" or "don't keep". A novel Conditional Random Field model is proposed to generate the distribution of sub-queries. This model captures the local and global dependencies between query words and directly optimizes the expected retrieval performance on a training set. The experiments, based on different retrieval models and performance measures, show that the proposed model can generate high-quality sub-query distributions and can significantly outperform state-of-the-art techniques.

Patent search is the task of finding relevant existing patents, which is an important part of the patent's examiner's process of validating a patent application. In this paper, we studied how to transform a query patent (the application) into search queries. Three types of search features are explored for automatic query generation for patent search. Furthermore, different types of features are combined with a learning to rank method. Experiments based on a USPTO patent collection demonstrate that the single best search feature is the combination of words and noun-phrases from the summary field and the retrieval performance can be significantly improved by combining three types of search features.

Searching for prior-art patents is an essential step for the patent examiner to validate or invalidate a patent application. In this paper, we consider the whole patent as the query, which reduces the burden on the user, and also makes many more potential search features available. We explore how to automatically transform the query patent into an effective search query, especially focusing on the effect of different patent fields. Experiments show that the background summary of a patent is the most useful source of terms for generating a query, even though most previous work used the patent claims.

Retrieval in a question and answer archive involves finding good answers for a user's question. In contrast to typical document retrieval, a retrieval model for this task can exploit question similarity as well as ranking the associated answers. In this paper, we propose a retrieval model that combines a translation-based language model for the question part with a query likelihood approach for the answer part. The proposed model incorporates word-to-word translation probabilities learned through exploiting different sources of information. Experiments show that the proposed translation based language model for the question part outperforms baseline methods significantly. By combining with the query likelihood language model for the answer part, substantial additional effectiveness improvements are obtained.