Related papers: RelEmb: A relevance-based application embedding fo…

This paper describes an efficiently scalable approach to measure technological similarity between patents by combining embedding techniques from natural language processing with nearest-neighbor approximation. Using this methodology we are…

Leveraging Large Language Models as Recommenders (LLMRec) has gained significant attention and introduced fresh perspectives in user preference modeling. Existing LLMRec approaches prioritize text semantics, usually neglecting the valuable…

"App stores" are online software stores where end users may browse, purchase, download, and install software applications. By far, the best known app stores are associated with mobile platforms, such as Google Play for Android and Apple's…

Many modern multiclass and multilabel problems are characterized by increasingly large output spaces. For these problems, label embeddings have been shown to be a useful primitive that can improve computational and statistical efficiency.…

Location sharing applications are becoming increasingly common. These applications allow users to share their own locations and view contacts' current locations on a map. Location applications are commonly used by friends and family members…

We have carefully instrumented a large portion of the population living in a university graduate dormitory by giving participants Android smart phones running our sensing software. In this paper, we propose the novel problem of predicting…

Candidate retrieval is the first stage in recommendation systems, where a light-weight system is used to retrieve potentially relevant items for an input user. These candidate items are then ranked and pruned in later stages of recommender…

Deep learning based techniques have been recently used with promising results for data integration problems. Some methods directly use pre-trained embeddings that were trained on a large corpus such as Wikipedia. However, they may not…

Routine and consistent data collection is required to address contemporary transportation issues.The cost of data collection increases significantly when sophisticated machines are used to collect data. Due to this constraint, State…

Information seeking on mobile devices is often fragmented, trapping users in repetitive cycles of context switching and data re-entry, which increases cognitive load and disrupts workflow. Existing mobile agents provide limited cross-source…

Neighborhoods populated by amenities--such as restaurants, cafes, and libraries--are considered to be a key property of desirable cities. Yet, despite the global enthusiasm for amenity-rich neighborhoods, little is known about the empirical…

Comprehensibility of source code is strongly affected by identifier names, therefore software developers need to give good (e.g. meaningful but short) names to identifiers. On the other hand, giving a good name is sometimes a difficult and…

In this paper we present APEX-Embedding-7B (Advanced Processing for Epistemic eXtraction), a 7-billion parameter decoder-only text Feature Extraction Model, specifically designed for Document Retrieval-Augmented Generation (RAG) tasks. Our…

The Experience Sampling Method (ESM) introduces in-situ sampling of human behaviour, and provides researchers and behavioural therapists with ecologically valid and timely assessments of a person's psychological state. This, in turn, opens…

In this paper we investigate the problem of localizing a mobile device based on readings from its embedded sensors utilizing machine learning methodologies. We consider a real-world environment, collect a large dataset of 3110 datapoints,…

Sentence embeddings are crucial in measuring semantic similarity. Most recent studies employed large language models (LLMs) to learn sentence embeddings. Existing LLMs mainly adopted autoregressive architecture without explicit backward…

Low-latency applications like AR/VR and online gaming need fast, stable connections. New technologies such as V2X, LEO satellites, and 6G bring unique challenges in mobility management. Traditional solutions based on centralized or…

Concept Bottleneck Models (CBMs) aim to enhance interpretability by predicting human-understandable concepts as intermediates for decision-making. However, these models often face challenges in ensuring reliable concept representations,…

Deploying deep learning (DL) on mobile devices has been a notable trend in recent years. To support fast inference of on-device DL, DL libraries play a critical role as algorithms and hardware do. Unfortunately, no prior work ever dives…

We study the task of retrieving relevant experiments given a query experiment. By experiment, we mean a collection of measurements from a set of `covariates' and the associated `outcomes'. While similar experiments can be retrieved by…