Exploring demographic information in social media for product recommendation

In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines.

Mining product adopter information from online reviews for improving product recommendation

We present in this article an automated framework that extracts product adopter information from online reviews and incorporates the extracted information into feature-based matrix factorization formore effective product recommendation. In specific, we propose a bootstrapping approach for the extraction of product adopters from review text and categorize them into a number of different demographic categories. The aggregated demographic information of many product adopters can be used to characterize both products and users in the form of distributions over different demographic categories. We further propose a graphbased method to iteratively update user- and product-related distributions more reliably in a heterogeneous user-product graph and incorporate them as features into the matrix factorization approach for product recommendation. Our experimental results on a large dataset crawled from JINGDONG, the largest B2C e-commerce website in China, show that our proposed framework outperforms a number of competitive baselines for product recommendation.

Versenyző együttműködés és a turisztikai desztinációk versenyképessége - "Hol volt, hol nem volt...?" (Co-opetition and the competitiveness of touristic destinations - "Where was, where it was not...?")

A versenyző együttműködés (co-opetition) népszerű fogalma számos elméleti alátámasztást nyert a versenyképesség elméleti megközelítéseiben is, így a regionális versenyképesség és a klaszterek elméleti alapvetéseinek területén. Maga a fogalom pedig kifejezetten „divatossá” vált hazánk turizmusában: a közelmúltban kerültek a versenyző együttműködést és a turisztikai versenyképességet szolgáló desztináció-menedzsment szervezetek (Turisztikai Desztináció Menedzsment Szervezetek, TDMSZ a továbbiakban) a turisztikai irányításpolitika és a turisztikai szakma figyelmének középpontjába. Jelen cikkben a szerző célja a versenyző együttműködés és a turisztikai desztinációk versenyképessége közötti elméleti összefüggés feltárása. További cél az elméleti alapvetések primer kutatás során való vizsgálata: az együttműködés mintáinak feltárása a desztinációk szereplői között három (egy hazai és két ausztriai) esettanulmány keretében, a hazai és nemzetközi esetek specifikumainak, valamint a desztinációk fejlettsége miatti kritikus különbségeknek a kimutatása által. / === / The popular term of co-opetition gained theoretical support even in the theoretical approaches of the area of competitiveness, particularly in the field of regional competitiveness and the clusters.The term became rather popular in Hungary: as the tools of destination management and the destination management organizations (DMOs) focusing on the development of co-opetition and competitiveness came to the focus of the Hungarian touristic practice, governmental decisions in the recent past. In this article the author’s aim is to describe the theoretical connections among co-opetition, and the competitiveness of the touristic destinations. Further aim is to analyze the theoretical baselines in primary research, to map the samples of cooperation among the actors of touristic destinations in three case studies (made in one Hungarian, and two Austrian destinations) regarding the topic with regard to Hungarian and international cases, as well as the stage of development.

Types and amplitudes of touch as reinforcing stimuli in operant conditioning of infant behavior

The reinforcing effects of diverse tactile stimuli were examined in this study. The study had two purposes. First, this study expanded on the Pelaez-Nogueras, Field, Gewirtz, Cigales, Gonzalez, Sanchez and Clasky (1997) finding that stroking increases infants' gaze duration, and smiling and vocalization frequencies more than tickling/poking. Instead of presenting poking and tickling as a single stimulus combination, this study separated poking and tickling in order to measure the effects of each component separately. Further, the effects of poking, tickling/tapping and stroking intensity (i.e., tactile pressure) were compared by having both mild and intense conditions. Second, this study compared the reinforcing efficacy of mother-delivered tactile stimulation to that of infant-originated tactile exploration. Twelve infants from 2- to 5-months of age participated in this study. The experiment was conducted using a repeated measures A-B-A-C-A-D reversal design. The A phases signified baselines and reversals. The B, C, and D phases consisted of alternating treatments (either mild stroking vs. mild poking vs. mild tickling/tapping, intense stroking vs. intense poking vs. intense tickling/tapping, or mother-delivered tactile stimulation vs. infant-originated tactile exploration). Three experimental hypotheses were assessed: (1) infant leg kick rate would be greater when it produced stroking or tickling/tapping (presumptive positive reinforcers), than when it produced poking (a possible punisher), regardless of tactile pressure; (2) infant leg kick rate would be greater when it produced a more intense level of stroking or tickling/tapping and lower when it produced intense poking compared to mild poking; (3) infant leg-kick rate would be greater for mother-delivered tactile stimulation than for infant-originated tactile exploration. Visual inspection and inferential statistical methods were used to analyze the results. The data supported the first two hypotheses. Mixed support emerged for the third hypothesis. This study made several important contributions to the field of psychology. First, this was the first study to quantify the pressure of tactile stimulation, via a pressure meter developed by the researcher. Additionally, the results of this study yielded valuable information about the effects of different modalities of touch. ^

Improving the Accuracy of 3-D Reconstruction in Robotic Vision Applications

Thesis (Ph.D.)--University of Washington, 2016-08

Microblogging Temporal Summarization: Filtering Important Twitter Updates for Breaking News

While news stories are an important traditional medium to broadcast and consume news, microblogging has recently emerged as a place where people can dis- cuss, disseminate, collect or report information about news. However, the massive information in the microblogosphere makes it hard for readers to keep up with these real-time updates. This is especially a problem when it comes to breaking news, where people are more eager to know “what is happening”. Therefore, this dis- sertation is intended as an exploratory effort to investigate computational methods to augment human effort when monitoring the development of breaking news on a given topic from a microblog stream by extractively summarizing the updates in a timely manner. More specifically, given an interest in a topic, either entered as a query or presented as an initial news report, a microblog temporal summarization system is proposed to filter microblog posts from a stream with three primary concerns: topical relevance, novelty, and salience. Considering the relatively high arrival rate of microblog streams, a cascade framework consisting of three stages is proposed to progressively reduce quantity of posts. For each step in the cascade, this dissertation studies methods that improve over current baselines. In the relevance filtering stage, query and document expansion techniques are applied to mitigate sparsity and vocabulary mismatch issues. The use of word embedding as a basis for filtering is also explored, using unsupervised and supervised modeling to characterize lexical and semantic similarity. In the novelty filtering stage, several statistical ways of characterizing novelty are investigated and ensemble learning techniques are used to integrate results from these diverse techniques. These results are compared with a baseline clustering approach using both standard and delay-discounted measures. In the salience filtering stage, because of the real-time prediction requirement a method of learning verb phrase usage from past relevant news reports is used in conjunction with some standard measures for characterizing writing quality. Following a Cranfield-like evaluation paradigm, this dissertation includes a se- ries of experiments to evaluate the proposed methods for each step, and for the end- to-end system. New microblog novelty and salience judgments are created, building on existing relevance judgments from the TREC Microblog track. The results point to future research directions at the intersection of social media, computational jour- nalism, information retrieval, automatic summarization, and machine learning.

Stable isotope ratios in bentho-demersal biota along a depth gradient in the bay of biscay: A multitrophic study

Although stable isotope ratios are increasingly used to investigate the trophic ecology of marine organisms, their spatial variations are still poorly understood in the coastal environment. In this study, we measured the stable isotope composition (δ13C, δ15N) of suspended particulate organic matter (SPOM) (primary producer), a suspension feeder, the great scallop Pecten maximus (primary consumer), megabenthic decapods and benthic fishes (secondary consumers) along a depth gradient (from 5m to 155m depth) across the continental shelf of the Bay of Biscay. Although the three trophic levels exhibited similar δ13C patterns along the gradient, the δ15N patterns varied between SPOM, scallops and carnivores. The δ15N difference between SPOM and scallops decreased with increasing depth, suggesting that non trophic factors may affect the stable isotope composition of scallops at deepest sampling stations. An opposed trend was found between scallops and carnivores, suggesting that the trophic level of these carnivores increased at higher depth, possibly as an adaptation to lower prey abundances. Although our results suggest that primary consumers are suitable to establish isotopic baselines in coastal environments, we stress the need for further studies aiming at characterizing the variability of stable isotopes in coastal biota, and the respective effects of baseline, trophic and metabolic factors in their isotopic composition.

THE FEASIBILITY OF USING WEB-BASED TECHNOLOGY FOR THE MANAGEMENT OF DREDGING PROJECTS

The research investigates the feasibility of using web-based project management systems for dredging. To achieve this objective the research assessed both the positive and negative aspects of using web-based technology for the management of dredging projects. Information gained from literature review and prior investigations of dredging projects revealed that project performance, social, political, technical, and business aspects of the organization were important factors in deciding to use web-based systems for the management of dredging projects. These factors were used to develop the research assumptions. An exploratory case study methodology was used to gather the empirical evidence and perform the analysis. An operational prototype of the system was developed to help evaluate developmental and functional requirements, as well as the influence on performance, and on the organization. The evidence gathered from three case study projects, and from a survey of 31 experts, were used to validate the assumptions. Baselines, representing the assumptions, were created as a reference to assess the responses and qualitative measures. The deviation of the responses was used to evaluate for the analysis. Finally, the conclusions were assessed by validating the assumptions with the evidence, derived from the analysis. The research findings are as follows: 1. The system would help improve project performance. 2. Resistance to implementation may be experienced if the system is implemented. Therefore, resistance to implementation needs to be investigated further and more R&D work is needed in order to advance to the final design and implementation. 3. System may be divided into standalone modules in order to simplify the system and facilitate incremental changes. 4. The QA/QC conceptual approach used by this research needs to be redefined during future R&D to satisfy both owners and contractors. Yin (2009) Case Study Research Design and Methods was used to develop the research approach, design, data collection, and analysis. Markus (1983) Resistance Theory was used during the assumptions definition to predict potential problems to the implementation of web-based project management systems for the dredging industry. Keen (1981) incremental changes and facilitative approach tactics were used as basis to classify solutions, and how to overcome resistance to implementation of the web-based project management system. Davis (1989) Technology Acceptance Model (TAM) was used to assess the solutions needed to overcome the resistances to the implementation of web-base management systems for dredging projects.

Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN

Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.

Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.

Autonomous formation flying: unified control and collision avoidance methods for close manoeuvring spacecraft

The idea of spacecraft formations, flying in tight configurations with maximum baselines of a few hundred meters in low-Earth orbits, has generated widespread interest over the last several years. Nevertheless, controlling the movement of spacecraft in formation poses difficulties, such as in-orbit high-computing demand and collision avoidance capabilities, which escalate as the number of units in the formation is increased and complicated nonlinear effects are imposed to the dynamics, together with uncertainty which may arise from the lack of knowledge of system parameters. These requirements have led to the need of reliable linear and nonlinear controllers in terms of relative and absolute dynamics. The objective of this thesis is, therefore, to introduce new control methods to allow spacecraft in formation, with circular/elliptical reference orbits, to efficiently execute safe autonomous manoeuvres. These controllers distinguish from the bulk of literature in that they merge guidance laws never applied before to spacecraft formation flying and collision avoidance capacities into a single control strategy. For this purpose, three control schemes are presented: linear optimal regulation, linear optimal estimation and adaptive nonlinear control. In general terms, the proposed control approaches command the dynamical performance of one or several followers with respect to a leader to asymptotically track a time-varying nominal trajectory (TVNT), while the threat of collision between the followers is reduced by repelling accelerations obtained from the collision avoidance scheme during the periods of closest proximity. Linear optimal regulation is achieved through a Riccati-based tracking controller. Within this control strategy, the controller provides guidance and tracking toward a desired TVNT, optimizing fuel consumption by Riccati procedure using a non-infinite cost function defined in terms of the desired TVNT, while repelling accelerations generated from the CAS will ensure evasive actions between the elements of the formation. The relative dynamics model, suitable for circular and eccentric low-Earth reference orbits, is based on the Tschauner and Hempel equations, and includes a control input and a nonlinear term corresponding to the CAS repelling accelerations. Linear optimal estimation is built on the forward-in-time separation principle. This controller encompasses two stages: regulation and estimation. The first stage requires the design of a full state feedback controller using the state vector reconstructed by means of the estimator. The second stage requires the design of an additional dynamical system, the estimator, to obtain the states which cannot be measured in order to approximately reconstruct the full state vector. Then, the separation principle states that an observer built for a known input can also be used to estimate the state of the system and to generate the control input. This allows the design of the observer and the feedback independently, by exploiting the advantages of linear quadratic regulator theory, in order to estimate the states of a dynamical system with model and sensor uncertainty. The relative dynamics is described with the linear system used in the previous controller, with a control input and nonlinearities entering via the repelling accelerations from the CAS during collision avoidance events. Moreover, sensor uncertainty is added to the control process by considering carrier-phase differential GPS (CDGPS) velocity measurement error. An adaptive control law capable of delivering superior closed-loop performance when compared to the certainty-equivalence (CE) adaptive controllers is finally presented. A novel noncertainty-equivalence controller based on the Immersion and Invariance paradigm for close-manoeuvring spacecraft formation flying in both circular and elliptical low-Earth reference orbits is introduced. The proposed control scheme achieves stabilization by immersing the plant dynamics into a target dynamical system (or manifold) that captures the desired dynamical behaviour. They key feature of this methodology is the addition of a new term to the classical certainty-equivalence control approach that, in conjunction with the parameter update law, is designed to achieve adaptive stabilization. This parameter has the ultimate task of shaping the manifold into which the adaptive system is immersed. The performance of the controller is proven stable via a Lyapunov-based analysis and Barbalat’s lemma. In order to evaluate the design of the controllers, test cases based on the physical and orbital features of the Prototype Research Instruments and Space Mission Technology Advancement (PRISMA) are implemented, extending the number of elements in the formation into scenarios with reconfigurations and on-orbit position switching in elliptical low-Earth reference orbits. An extensive analysis and comparison of the performance of the controllers in terms of total Δv and fuel consumption, with and without the effects of the CAS, is presented. These results show that the three proposed controllers allow the followers to asymptotically track the desired nominal trajectory and, additionally, those simulations including CAS show an effective decrease of collision risk during the performance of the manoeuvre.