Average Ranking Score Research Articles

INFORMATION FILTERING BASED ON USERS' NEGATIVE OPINIONS

The process of heat conduction (HC) has recently found application in the information filtering [Zhang et al., Phys. Rev. Lett.99, 154301 (2007)], which is of high diversity but low accuracy. The classical HC model predicts users' potential interested objects based on their interesting objects regardless to the negative opinions. In terms of the users' rating scores, we present an improved user-based HC (UHC) information model by taking into account users' positive and negative opinions. Firstly, the objects rated by users are divided into positive and negative categories, then the predicted interesting and dislike object lists are generated by the UHC model. Finally, the recommendation lists are constructed by filtering out the dislike objects from the interesting lists. By implementing the new model based on nine similarity measures, the experimental results for MovieLens and Netflix datasets show that the new model considering negative opinions could greatly enhance the accuracy, measured by the average ranking score, from 0.049 to 0.036 for Netflix and from 0.1025 to 0.0570 for Movielens dataset, reduced by 26.53% and 44.39%, respectively. Since users prefer to give positive ratings rather than negative ones, the negative opinions contain much more information than the positive ones, the negative opinions, therefore, are very important for understanding users' online collective behaviors and improving the performance of HC model.

Importance of Indigenous Knowledge in Biodiversity Conservation: A Case Study of Communities Surrounding Kpashimi Forest Reserve, Niger State, Nigeria

Forest resource use and traditional conservation practices were studied in nine forest neighbouring communities to Kpashimi Forest Reserve, Niger state. The aim of the study was to examine the importance of indigenous knowledge in biodiversity conservation among the communities surrounding Kpashimi Forest Reserve. Ecological survey and Rapid Rural Appraisal methods were adopted for the study. Matrix ranking and scoring was employed to analyse data on communities' utilization of vegetation species. The average rank score for each perceived important value was calculated. Medicinal value was ranked first with average rank score of 1.21, followed by fuel wood (2.33), food (2.62), cultural value (3.72), timber (3.86), building materials (3.92), grazing (5.24), aesthetic (7.10), fibre (7.34), and employment (8.22). Analysis of respondents' perceived relevance of indigenous knowledge on biodiversity conservation indicated weighted mean score statistics and standard deviation ranging between 3.33- 4.16 and 0.078 - 0.596, respectively. This implies that their mean values were all above the cut-off point of 2.50 and therefore recognised the importance of indigenous knowledge in biodiversity conservation. The study revealed that existing traditional conservation methods among the natives include preservation of sacred landscapes for threatened species, myths and taboos restricting use through dos and don'ts, harvesting methods, spiritual values associated with forests and individual species. Appreciating these immense values and threats from changing environment, the natives indicated approval for the integration of indigenous knowledge with modern forest management practices. The study therefore contends that for continued sustainability of forest biodiversity, conservation strategies need to recognise local knowledge in both content and practice.

INFORMATION FILTERING VIA CLUSTERING COEFFICIENTS OF USER–OBJECT BIPARTITE NETWORKS

The clustering coefficient of user–object bipartite networks is presented to evaluate the overlap percentage of neighbors rating lists, which could be used to measure interest correlations among neighbor sets. The collaborative filtering (CF) information filtering algorithm evaluates a given user's interests in terms of his/her friends' opinions, which has become one of the most successful technologies for recommender systems. In this paper, different from the object clustering coefficient, users' clustering coefficients of user–object bipartite networks are introduced to improve the user similarity measurement. Numerical results for MovieLens and Netflix data sets show that users' clustering effects could enhance the algorithm performance. For MovieLens data set, the algorithmic accuracy, measured by the average ranking score, can be improved by 12.0% and the diversity could be improved by 18.2% and reach 0.649 when the recommendation list equals to 50. For Netflix data set, the accuracy could be improved by 14.5% at the optimal case and the popularity could be reduced by 13.4% comparing with the standard CF algorithm. Finally, we investigate the sparsity effect on the performance. This work indicates the user clustering coefficients is an effective factor to measure the user similarity, meanwhile statistical properties of user–object bipartite networks should be investigated to estimate users' tastes.

Information filtering via weighted heat conduction algorithm

In this paper, by taking into account effects of the user and object correlations on a heat conduction (HC) algorithm, a weighted heat conduction (WHC) algorithm is presented. We argue that the edge weight of the user–object bipartite network should be embedded into the HC algorithm to measure the object similarity. The numerical results indicate that both the accuracy and diversity could be improved greatly compared with the standard HC algorithm and the optimal values reached simultaneously. On the Movielens and Netflix datasets, the algorithmic accuracy, measured by the average ranking score, can be improved by 39.7% and 56.1% in the optimal case, respectively, and the diversity could reach 0.9587 and 0.9317 when the recommendation list equals to 5. Further statistical analysis indicates that, in the optimal case, the distributions of the edge weight are changed to the Poisson form, which may be the reason why HC algorithm performance could be improved. This work highlights the effect of edge weight on a personalized recommendation study, which maybe an important factor affecting personalized recommendation performance.

PZIM: A Method for Similarity Searching Using Atom Environments and 2D Alignment

The advent of extensive small molecule databases has brought with it the problem of searching these repositories for entities with desired properties. A multitude of similarity-searching algorithms, based on different underlying methods, currently exist for this purpose. However, due to the somewhat nebulous definition of "similar", all such approaches maintain different strengths and weaknesses. Presented here is PZIM, a new approach fundamentally based on a description of the atom environment that includes multiple adjustable features allowing for searches to be tailored on the basis of the user definition of similarity. In addition to flexible atom environment size, PZIM allows for the use of an atom-substitution matrix to identify similar pharmacophoric recognition elements. Finally, PZIM produces 2-dimensional alignments of all compared molecules that pass a user-defined threshold for similarity. To determine the usefulness of the approach, PZIM was compared to seven other similarity-searching methods on nine data sets recently published. PZIM achieved a rank of first or second in the majority of cases tested and obtained the highest average rank score across all methods tested. These results demonstrate the effectiveness of the PZIM approach across diverse search conditions.

CLUSTERING EFFECT OF USER-OBJECT BIPARTITE NETWORK ON PERSONALIZED RECOMMENDATION

In this paper, the statistical property of the bipartite network, namely clustering coefficient C4 is taken into account and be embedded into the collaborative filtering (CF) algorithm to improve the algorithmic accuracy and diversity. In the improved CF algorithm, the user similarity is defined by the mass diffusion process, and we argue that the object clustering C4 of the bipartite network should be considered to improve the user similarity measurement. The statistical result shows that the clustering coefficient of the MovieLens data approximately has Poisson distribution. By considering the clustering effects of object nodes, the numerical simulation on a benchmark data set shows that the accuracy of the improved algorithm, measured by the average ranking score and precision, could be improved 15.3 and 13.0%, respectively, in the optimal case. In addition, numerical results show that the improved algorithm can provide more diverse recommendation results, for example, when the recommendation list contains 20 objects, the diversity, measured by the hamming distance, is improved by 28.7%. Since all of the real recommendation data are evolving with time, this work may shed some light on the adaptive recommendation algorithm according to the statistical properties of the user-object bipartite network.

DEGREE CORRELATION OF BIPARTITE NETWORK ON PERSONALIZED RECOMMENDATION

In this paper, the statistical property, namely degree correlation between users and objects, is taken into account and be embedded into the similarity index of collaborative filtering (CF) algorithm to improve the algorithmic performance. The numerical simulation on a benchmark data set shows that the algorithmic accuracy of the presented algorithm, measured by the average ranking score, is improved by 18.19% in the optimal case. The statistical analysis on the product distribution of the user and object degrees indicate that, in the optimal case, the distribution obeys the power-law and the exponential is equal to -2.33. Numerical results show that the presented algorithm can provide more diverse and less popular recommendations, for example, when the recommendation list contains 10 objects, the diversity, measured by the hamming distance, is improved by 21.90%. Since all of the real recommendation data evolving with time, this work may shed some light on the adaptive recommendation algorithm which could change its parameter automatically according to the statistical properties of the user-object bipartite network.

EFFECTS OF USER'S TASTES ON PERSONALIZED RECOMMENDATION

In this paper, based on a weighted projection of the user-object bipartite network, we study the effects of user tastes on the mass-diffusion-based personalized recommendation algorithm, where a user's tastes or interests are defined by the average degree of the objects he has collected. We argue that the initial recommendation power located on the objects should be determined by both of their degree and the user's tastes. By introducing a tunable parameter, the user taste effects on the configuration of initial recommendation power distribution are investigated. The numerical results indicate that the presented algorithm could improve the accuracy, measured by the average ranking score. More importantly, we find that when the data is sparse, the algorithm should give more recommendation power to the objects whose degrees are close to the user's tastes, while when the data becomes dense, it should assign more power on the objects whose degrees are significantly different from user's tastes.

Effects of high-order correlations on personalized recommendations for bipartite networks

In this paper, we introduce a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the MCF, instead of the cosine similarity index, the user–user correlations are obtained by a diffusion process. Furthermore, by considering the second-order correlations, we design an effective algorithm that depresses the influence of mainstream preferences. Simulation results show that the algorithmic accuracy, measured by the average ranking score, is further improved by 20.45% and 33.25% in the optimal cases of MovieLens and Netflix data. More importantly, the optimal value λ opt depends approximately monotonously on the sparsity of the training set. Given a real system, we could estimate the optimal parameter according to the data sparsity, which makes this algorithm easy to be applied. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that as the sparsity increases, the algorithm considering the second-order correlation can outperform the MCF simultaneously in all three criteria.

An Investigation of Radiologists' Perception of Lesion Similarity: Observations with Paired Breast Masses on Mammograms and Paired Lung Nodules on CT Images

We conducted an observer study to investigate whether radiologists can judge similarities in pairs of breast masses and lung nodules consistently and reproducibly. Institutional review board approval and informed observer consent were obtained. This study was compliant with the Health Insurance Portability and Accountability Act. We used eight pairs of breast masses on mammograms and eight pairs of lung nodules on computed tomographic images, for which subjective similarity ratings ranging from 0 to 1 were determined in our previous studies. From these, four sets of image pairs were created (ie, a set of eight mass pairs, a set of eight nodule pairs, and two mixed sets of four mass and four nodule pairs). Eight radiologists, including four breast radiologists and four chest radiologists, compared all combinations of the eight pairs in each set using a two-alternative forced-choice (2AFC) method to determine the similarity ranking scores by identifying which pair was more similar than the other pair based on the overall impression for diagnosis. In the mass set and nodule set, the relationship between the average subjective similarity ratings and the average similarity ranking scores by 2AFC indicated very high correlations (r = 0.91 and 0.88). Moreover, in the two mixed sets, the correlations between the average subjective similarity ratings and the average similarity ranking scores were also very high (r = 0.90 and 0.98). Thus, radiologists were able to compare the similarities for pairs of lesions consistently, even in the unusual comparison of pairs of completely different types of lesions. The subjective similarity of a pair of lesions in medical images can be quantified consistently by a group of radiologists. The concept of similarity of lesions in medical images can be subjected to rigorous scientific research and investigation in the future.

Determination of subjective similarity for pairs of masses and pairs of clustered microcalcifications on mammograms: Comparison of similarity ranking scores and absolute similarity ratings

The presentation of images that are similar to that of an unknown lesion seen on a mammogram may be helpful for radiologists to correctly diagnose that lesion. For similar images to be useful, they must be quite similar from the radiologists' point of view. We have been trying to quantify the radiologists' impression of similarity for pairs of lesions and to establish a "gold standard" for development and evaluation of a computerized scheme for selecting such similar images. However, it is considered difficult to reliably and accurately determine similarity ratings, because they are subjective. In this study, we compared the subjective similarities obtained by two different methods, an absolute rating method and a 2-alternative forced-choice (2AFC) method, to demonstrate that reliable similarity ratings can be determined by the responses of a group of radiologists. The absolute similarity ratings were previously obtained for pairs of masses and pairs of microcalcifications from five and nine radiologists, respectively. In this study, similarity ranking scores for eight pairs of masses and eight pairs of microcalcifications were determined by use of the 2AFC method. In the first session, the eight pairs of masses and eight pairs of microcalcifications were grouped and compared separately for determining the similarity ranking scores. In the second session, another similarity ranking score was determined by use of mixed pairs, i.e., by comparison of the similarity of a mass pair with that of a calcification pair. Four pairs of masses and four pairs of microcalcifications were grouped together to create two sets of eight pairs. The average absolute similarity ratings and the average similarity ranking scores showed very good correlations in the first study (Pearson's correlation coefficients: 0.94 and 0.98 for masses and microcalcifications, respectively). Moreover, in the second study, the correlations between the absolute ratings and the ranking scores were also very high (0.92 and 0.96), which implies that the observers were able to compare the similarity of a mass pair with that of a calcification pair consistently. These results provide evidence that the concept of similarity for pairs of images is robust, even across different lesion types, and that radiologists are able to reliably determine subjective similarity for pairs of breast lesions.

SU‐FF‐I‐01: Determination of Subjective Similarity for Pairs of Lesions On Mammograms: Comparison of Ranking Scores in 2AFC Versus Absolute Ratings for Masses and Microcalcifications

Purpose: We previously obtained the subjective similarity ratings for pairs of lesions on mammograms for quantitative evaluation of similar images. Our purpose in this study was to investigate whether the absolute similarity ratings can be determined reliably by comparison with ranking scores obtained in a 2‐alternative forced‐choice (2AFC) method. Method and Materials: We selected 8 pairs of masses and 8 pairs of clustered microcalcifications based on radiologists' average similarity ratings; similarity ratings for the two sets of 8 pairs were approximately evenly distributed. In the first study, each pair was compared one by one to all other 7 pairs in each group of masses and microcalcifications. In the second study, we combined four pairs of masses and four pairs of microcalcifications to compare the similarity of a pair of masses with that of microcalcifications. Seven radiologists and 3 senior residents were asked to choose one pair that was more similar than another pair with the 2AFC method. The cases were presented in randomized order. The number of times that a pair was selected as more similar was counted as the subjective ranking score. The average scores were compared with the average similarity ratings determined previously. Results: The average ranking scores from the first study were highly correlated (0.93 and 0.98 for masses and calcifications, respectively) with absolute similarity ratings. When mass pairs were compared with calcification pairs, the correlations between ranking scores and absolute ratings were also very high (0.92 and 0.96). In both studies, observers were very consistent in selecting more similar pairs. Conclusion: The result indicates that absolute similarity ratings determined previously are reliable and useful for selection of similar images. The concept of similarity is robust and meaningful even when mass pairs are compared with microcalcification pairs.

Flat-Panel–Detector Chest Radiography: Effect of Tube Voltage on Image Quality

To compare the visibility of anatomic structures in direct-detector chest radiographs acquired with different tube voltages at equal effective doses to the patient. The study protocol was approved by the institutional internal review board, and written informed consent was obtained from all patients. Posteroanterior chest radiographs of 48 consecutively selected patients were obtained at 90, 121, and 150 kVp by using a flat-panel-detector unit that was based on cesium iodide technology and automated exposure control. Monte Carlo simulations were used to verify that the effective dose for all kilovoltage settings was equal. Five radiologists subjectively and independently rated the delineation of anatomic structures on hard-copy images by using a five-point scale. They also ranked image quality in a blinded side-by-side comparison. Average ranking scores were compared by using one-way analysis of variance with repeated measures. Data were analyzed for the entire patient group and for two patient subgroups that were formed according to body mass index (BMI). The visibility scores of most anatomic structures were significantly superior with the 90-kVp images (mean score, 3.11), followed by the 121-kVp (mean score, 2.95) and 150-kVp images (mean score, 2.80). Differences did not reach significance (P > .05) only for the delineation of the peripheral vessels, the heart contours, and the carina. This was also true for the subgroup of patients (n = 24) with a BMI greater than and the subgroup of patients (n = 24) with a BMI less than the mean BMI (26.9 kg/m(2)). At side-by-side comparison, the readers rated 90-kVp images as having superior image quality in the majority of image triplets; the percentage of 90-kVp images rated as "first choice" ranged from 60% (29 of 48 patients) to 90% (43 of 48 patients), with a median of 88% (42 of 48 patients), among the readers. Delineation of most anatomic structures and overall image quality were ranked superior in digital radiographs acquired with lower kilovoltage at a constant effective patient dose.

Hazard potential ranking of hazardous waste landfill sites and risk of congenital anomalies

Background: A 33% increase in the risk of congenital anomalies has been found among residents near hazardous waste landfill sites in a European collaborative study (EUROHAZCON). Aims: To develop and...

The duckweed Lemna minor compared with the alga Selenastrum capricornutum for bioassay of pond-water richness

In an initial comparison of Lemna and Selenastrum bioassay, on water from two ponds, both organisms made greater growth in the more nutrient-rich water. However, when the comparison was extended to 57 diverse ponds, the two bioassay organisms responded to different aspects of water quality. Lemna and Selenastrum growth were not correlated, and were not necessarily related to the same water-quality variables: e.g. Lemna dry weight was correlated with Ca, Mg and K while Selenastrum cell concentration was correlated with PO 4, NH 4, NO 2 and (negatively) with pH. Neither Lemna nor Selenastrum growth were related to the number of hydrophyte species or their average trophic ranking score. Thus, bioassay to assess pond richness, either using Lemna or Selenastrum, is not straightforward and is probably best used alongside water analysis and/or vegetation survey.