Jointly Tackling User and Item Cold-start with Sequential Contentbased Recommendations

Sügavaid närvivõrke on edukalt kasutatud mitmetes soovitussüsteemides. Sessioonipõhised soovitussüsteemid on nende üks alaliik, milles modelleeritakse kasutajate ja toodete interaktsioone (klikke), selleks et genereerida kasutajale isikupäraseid soovitusi. Rekurrentsed närvivõrgud on viimastel aastatel muutunud eelistatuimaks lahenduseks mitmesuguste jadaandmete modelleerimisel, sh kasutajasessioonid, kuid olemasolevate lahenduste puuduseks on see, et need on jäigalt seotud tootekataloogi ja selles olevate toodetega. Uute toodete lisandumisel tuleb kogu mudel uuesti treenida. Üks võimalik lahendus sellele on toodete metainfo (pealkiri, kirjeldus, pilt) kasutuselevõtt, mis võimaldab tooteid identifitseerida nende sisu põhjal, mitte identifikaatori järgi. Samas teadaolevalt ei ole hetkel välja pakutud meetodit, mis lahendaks korraga nii uue toote kui ka uue kasutaja lisandumise probleemi sessioonipõhistes soovitussüsteemides.Töös pakutakse välja uudne arhitektuur sessioonipõhise soovitussüsteemi jaoks, mis kasutab toodete metainfol põhinevaid vektoresitusi. Mudelis kombineeritakse sessiooni jooksul külastatud toodete vektoresitused, selleks et ennustada järgmise toote vektoresitust. Selline lahendus võimaldab lisada tootekataloogi uusi tooteid ilma mudelit uuesti treenimata. Täiendavalt kasutatakse kasutaja sessiooni tema eelistuste modelleerimiseks, mis tähendab, et ennustatud järgmine toode sõltub kasutaja varasematest interaktsioonidest ja seega on tegemist isikupärase ennustusega. Eksperimendid viidi läbi Amazoni kasutajaarvustuste andmestiku peal ning tulemusi võrreldi GRU4Rec ja TransRec mudelitega. Pakutud lahendus saavutas võrreldavaid või paremaid tulemusi kui varasemad parimad mudelid ning võimaldab seejuures lihtsustada uute toodete või kasutajate lisamist.Deep learning has been successfully used in the context of recommender systems. Sequential recommender systems are a class of algorithms which model user-item interactions and their temporal relationship in order to generate relevant personalized recommendations. Recurrent neural networks have become the state-of-the-art approach for sequential modeling, but current approaches in the context of recommendation systems are tightly coupled with the catalog size and item identifiers. This imposes a problem when new items are to be incorporated into the list of recommendable products, the entire model needs to be retrained. Feature-rich item metadata has been successfully used to improve recommendation quality with both sequential and non-sequential recommenders. However, to the best of our knowledge, no attempt has been made to tackle the problem of newly encountered user and item in a sequence aware model with personalized recommendations. This work presents a novel architecture for context-aware item prediction based on embeddings. The model combines item embeddings within a sequence to dynamically predict an item embedding for the next interaction. This allows to incorporate new items without model retraining. Moreover, the proposed architecture implicitly models the user preferences from user-item interactions and is able to provide item embedding predictions that are personalized to the context of a user and therefore produce personalized recommendations. The results are compared with GRU4Rec and TransRec in the next interaction prediction task using the Amazon reviews public dataset, and our experiments show comparable or better results than state-of-the-art personalized models, with the added benefit of being able to add items or users without model retraining

Low-cost deep learning UAV and Raspberry Pi solution to real time pavement condition assessment

In this thesis, a real-time and low-cost solution to the autonomous condition assessment of pavement is proposed using deep learning, Unmanned Aerial Vehicle (UAV) and Raspberry Pi tiny computer technologies, which makes roads maintenance and renovation management more efficient and cost effective. A comparison study was conducted to compare the performance of seven different combinations of meta-architectures for pavement distress classification. It was observed that real-time object detection architecture SSD with MobileNet feature extractor is the best combination for real-time defect detection to be used by tiny computers. A low-cost Raspberry Pi smart defect detector camera was configured using the trained SSD MobileNet v1, which can be deployed with UAV for real-time and remote pavement condition assessment. The preliminary results show that the smart pavement detector camera achieves an accuracy of 60% at 1.2 frames per second in raspberry pi and 96% at 13.8 frames per second in CPU-based computer

TriMLP: Revenge of a MLP-like Architecture in Sequential Recommendation

In this paper, we present a MLP-like architecture for sequential recommendation, namely TriMLP, with a novel Triangular Mixer for cross-token communications. In designing Triangular Mixer, we simplify the cross-token operation in MLP as the basic matrix multiplication, and drop the lower-triangle neurons of the weight matrix to block the anti-chronological order connections from future tokens. Accordingly, the information leakage issue can be remedied and the prediction capability of MLP can be fully excavated under the standard auto-regressive mode. Take a step further, the mixer serially alternates two delicate MLPs with triangular shape, tagged as global and local mixing, to separately capture the long range dependencies and local patterns on fine-grained level, i.e., long and short-term preferences. Empirical study on 12 datasets of different scales (50K\textasciitilde 10M user-item interactions) from 4 benchmarks (Amazon, MovieLens, Tenrec and LBSN) show that TriMLP consistently attains promising accuracy/efficiency trade-off, where the average performance boost against several state-of-the-art baselines achieves up to 14.88% with 8.65% less inference cost.Comment: 15 pages, 9 figures, 5 table

Enhancing Prediction Reliability Of Deep Learning By Data Confidence For Recommendation Systems: A Case Study On Named Entity Recognition

Reliability is crucial for industrial recommendation systems. Recent advancement in deep neural networks has greatly improved the performance of modern recommendation systems. However, there is a lack of research on estimating how reliable such recommendation systems are in practical scenarios. Due to the blackbox nature of the deep learning-based systems, many times additional labor has to be involved to examine the prediction accuracy manually, which is costly and time-consuming. To address the problem, we propose a novel approach to estimate the model confidence for a deep learning-based recommendation system. Our approach utilized data statistics to improve the traditional model confidence estimation and maintain the model’s high performance. We further proposed a new evaluation metric to properly compare different prediction confidence estimation approaches. Experimental results showed that the external data statistics could effectively improve the prediction reliability by increasing confidence score, which will lead to significant reduction of the time and labors on the system’s prediction result examination. Index Terms—Prediction Reliability, Recommendation Systems, Deep Learning, Data Confidence, Named Entity Recognitio

UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language

We introduce UbiPhysio, a milestone framework that delivers fine-grained action description and feedback in natural language to support people's daily functioning, fitness, and rehabilitation activities. This expert-like capability assists users in properly executing actions and maintaining engagement in remote fitness and rehabilitation programs. Specifically, the proposed UbiPhysio framework comprises a fine-grained action descriptor and a knowledge retrieval-enhanced feedback module. The action descriptor translates action data, represented by a set of biomechanical movement features we designed based on clinical priors, into textual descriptions of action types and potential movement patterns. Building on physiotherapeutic domain knowledge, the feedback module provides clear and engaging expert feedback. We evaluated UbiPhysio's performance through extensive experiments with data from 104 diverse participants, collected in a home-like setting during 25 types of everyday activities and exercises. We assessed the quality of the language output under different tuning strategies using standard benchmarks. We conducted a user study to gather insights from clinical experts and potential users on our framework. Our initial tests show promise for deploying UbiPhysio in real-life settings without specialized devices.Comment: 27 pages, 14 figures, 5 table

A personalized hybrid music recommender based on empirical estimation of user-timbre preference

Automatic recommendation system as a subject of machine learning has been undergoing a rapid development in the recent decade along with the trend of big data. Particularly, music recommendation is a highlighted topic because of its commercial value coming from the large music industry. Popular online music recommendation services, including Spotify, Pandora and Last.FM use similarity-based approaches to generate recommendations. In this thesis work, I propose a personalized music recommendation approach that is based on probability estimation without any similarity calculation involved. In my system, each user gets a score for every piece of music. The score is obtained by combining two estimated probabilities of an acceptance. One estimated probability is based on the user’s preferences on timbres. Another estimated probability is the empirical acceptance rate of a music piece. The weighted arithmetic mean is evaluated to be the best performing combination function. An online demonstration of my system is available at www.shuyang.eu/plg/. Demonstrating recommendation results show that the system works effectively. Through the algorithm analysis on my system, we can see that my system has good reactivity and scalability without suffering cold start problem. The accuracy of my recommendation approach is evaluated with Million Song Dataset. My system achieves a pairwise ranking accuracy of 0.592, which outperforms random ranking (0.5) and ranking by popularity (0.557). Unfortunately, I have not found any other music recommendation method evaluated with ranking accuracy yet. As a comparison, Page Rank algorithm (for web page ranking) has a pairwise ranking accuracy of 0.567