A Formal Model of Information Retrieval Based on User Sensitivities

AbstractSearch engines are a very important web applications used by millions of users around the world on a daily basis to search the Web. Finding relevant information in this growing space is challenging, and is complicated by the diversity and needs of the community of Web users. Indeed, the Web is one, but the needs of users are multiple and different. Thus, information relevancy is not only related to the formulated query, but also to the user who is formulating this query. For example, user sensitivities may enhance information relevancy. In this paper, we are proposing to derive a formal model of user sensitivities integration into search engines

Tenzorske reprezentacije hipergrafa i primjene u analizi društvenih mreža

U ovom radu iznesene su osnovne definicije vezane uz tenzore. Detaljno je opisana notacija i operacije nad tenzorima. Dotaknuto je određivanje ranga tenzora kao i razlike u usporedbi svojstava tenzorskog s matričnim rangom (rang tenzora može biti različit nad $\mathbb{R}$ i nad $\mathbb{C}$, problem određivanja ranga tenzora je NP-težak). Nakon toga rad se bavi određivanjem PARAFAC dekompozicije tenzora trećeg reda, dok je za tenzore višeg reda iznesen pseudokod na Slici 1.7. Također, ovom radu je predstavljen TweetRank model, novi pristup rangiranju autoriteta u zajednici društvenih mreža. Koncepcijski, TweetRank je dodatak metodama za rangiranje autoriteta znanih iz web pretraživanja kao što su PageRank i HITS. Ovaj pristup iskorištava novi reprezentacijski model za društvene grafove temeljen na trodimenzionalnim tenzorima. To nam omogućuje da prirodnim putem iskoristimo semantiku korisničkih relacija. Primjenom PARAFAC tenzorske dekompozicije identificiramo autoritativne izvore u društvenoj mreži kao i grupe semantički usko povezanih pojmova od interesa. Iz tog razloga TweetRank model možemo smatrati sljedećim korakom prema učinkovitijoj i djelotvornijoj tehnologiji pretraživanja/preporučivanja za društvenu mrežu.In this work we presented basic definitions related with tensors. Specified description is made for notation and tensor operations. We also observed determination of tensor rank and differences in comparation between property of tensor and matrix rank (tensor rank can be different over $\mathbb{R}$ and over $\mathbb{C}$, the problem of determining tensor rank is NP-hard). After that this work deals with determining PARAFAC tensor decomposition of third-order tensors, while for the higher-order tensors is presented pseudocode on Figure 1.7. Also, in this work we presented TweetRank, a novel approach for authority ranking in SocialWeb communities. Conceptually, TweetRank is a correspondent to authority ranking methods known from Web retrieval, such as PageRank or HITS. This approach exploits the novel representational model for social graphs, based on 3-dimensional tensors. This allows us to exploit in the natural way the available semantics of user relations. By applying the PARAFAC tensor decomposition we identify authoritative sources in the social network as well as groups of semantically coherent terms of interest. Therefore, TweetRank can be seen as a next step towards efficient and effective search/recommendation technology for the Social Web

Tenzorske reprezentacije hipergrafa i primjene u analizi društvenih mreža

U ovom radu iznesene su osnovne definicije vezane uz tenzore. Detaljno je opisana notacija i operacije nad tenzorima. Dotaknuto je određivanje ranga tenzora kao i razlike u usporedbi svojstava tenzorskog s matričnim rangom (rang tenzora može biti različit nad $\mathbb{R}$ i nad $\mathbb{C}$, problem određivanja ranga tenzora je NP-težak). Nakon toga rad se bavi određivanjem PARAFAC dekompozicije tenzora trećeg reda, dok je za tenzore višeg reda iznesen pseudokod na Slici 1.7. Također, ovom radu je predstavljen TweetRank model, novi pristup rangiranju autoriteta u zajednici društvenih mreža. Koncepcijski, TweetRank je dodatak metodama za rangiranje autoriteta znanih iz web pretraživanja kao što su PageRank i HITS. Ovaj pristup iskorištava novi reprezentacijski model za društvene grafove temeljen na trodimenzionalnim tenzorima. To nam omogućuje da prirodnim putem iskoristimo semantiku korisničkih relacija. Primjenom PARAFAC tenzorske dekompozicije identificiramo autoritativne izvore u društvenoj mreži kao i grupe semantički usko povezanih pojmova od interesa. Iz tog razloga TweetRank model možemo smatrati sljedećim korakom prema učinkovitijoj i djelotvornijoj tehnologiji pretraživanja/preporučivanja za društvenu mrežu.In this work we presented basic definitions related with tensors. Specified description is made for notation and tensor operations. We also observed determination of tensor rank and differences in comparation between property of tensor and matrix rank (tensor rank can be different over $\mathbb{R}$ and over $\mathbb{C}$, the problem of determining tensor rank is NP-hard). After that this work deals with determining PARAFAC tensor decomposition of third-order tensors, while for the higher-order tensors is presented pseudocode on Figure 1.7. Also, in this work we presented TweetRank, a novel approach for authority ranking in SocialWeb communities. Conceptually, TweetRank is a correspondent to authority ranking methods known from Web retrieval, such as PageRank or HITS. This approach exploits the novel representational model for social graphs, based on 3-dimensional tensors. This allows us to exploit in the natural way the available semantics of user relations. By applying the PARAFAC tensor decomposition we identify authoritative sources in the social network as well as groups of semantically coherent terms of interest. Therefore, TweetRank can be seen as a next step towards efficient and effective search/recommendation technology for the Social Web

Tenzorske reprezentacije hipergrafa i primjene u analizi društvenih mreža

U ovom radu iznesene su osnovne definicije vezane uz tenzore. Detaljno je opisana notacija i operacije nad tenzorima. Dotaknuto je određivanje ranga tenzora kao i razlike u usporedbi svojstava tenzorskog s matričnim rangom (rang tenzora može biti različit nad $\mathbb{R}$ i nad $\mathbb{C}$, problem određivanja ranga tenzora je NP-težak). Nakon toga rad se bavi određivanjem PARAFAC dekompozicije tenzora trećeg reda, dok je za tenzore višeg reda iznesen pseudokod na Slici 1.7. Također, ovom radu je predstavljen TweetRank model, novi pristup rangiranju autoriteta u zajednici društvenih mreža. Koncepcijski, TweetRank je dodatak metodama za rangiranje autoriteta znanih iz web pretraživanja kao što su PageRank i HITS. Ovaj pristup iskorištava novi reprezentacijski model za društvene grafove temeljen na trodimenzionalnim tenzorima. To nam omogućuje da prirodnim putem iskoristimo semantiku korisničkih relacija. Primjenom PARAFAC tenzorske dekompozicije identificiramo autoritativne izvore u društvenoj mreži kao i grupe semantički usko povezanih pojmova od interesa. Iz tog razloga TweetRank model možemo smatrati sljedećim korakom prema učinkovitijoj i djelotvornijoj tehnologiji pretraživanja/preporučivanja za društvenu mrežu.In this work we presented basic definitions related with tensors. Specified description is made for notation and tensor operations. We also observed determination of tensor rank and differences in comparation between property of tensor and matrix rank (tensor rank can be different over $\mathbb{R}$ and over $\mathbb{C}$, the problem of determining tensor rank is NP-hard). After that this work deals with determining PARAFAC tensor decomposition of third-order tensors, while for the higher-order tensors is presented pseudocode on Figure 1.7. Also, in this work we presented TweetRank, a novel approach for authority ranking in SocialWeb communities. Conceptually, TweetRank is a correspondent to authority ranking methods known from Web retrieval, such as PageRank or HITS. This approach exploits the novel representational model for social graphs, based on 3-dimensional tensors. This allows us to exploit in the natural way the available semantics of user relations. By applying the PARAFAC tensor decomposition we identify authoritative sources in the social network as well as groups of semantically coherent terms of interest. Therefore, TweetRank can be seen as a next step towards efficient and effective search/recommendation technology for the Social Web

A data-driven situation-aware framework for predictive analysis in smart environments

In the era of Internet of Things (IoT), it is vital for smart environments to be able to efficiently provide effective predictions of user’s situations and take actions in a proactive manner to achieve the highest performance. However, there are two main challenges. First, the sensor environment is equipped with a heterogeneous set of data sources including hardware and software sensors, and oftentimes complex humans as sensors, too. These sensors generate a huge amount of raw data. In order to extract knowledge and do predictive analysis, it is necessary that the raw sensor data be cleaned, understood, analyzed, and interpreted. Second challenge refers to predictive modeling. Traditional predictive models predict situations that are likely to happen in the near future by keeping and analyzing the history of past user’s situations. Traditional predictive analysis approaches have become less effective because of the massive amount of data that both affects data processing efficiency and complicates the data semantics. In this study, we propose a data-driven, situation-aware framework for predictive analysis in smart environments that addresses the above challenges