This paper adapts models from the area of spatial statistics to the task of predicting a user's interests (i. e., implicit item ratings) within a recommender system in the museum domain. We develop a model based on Gaussian spatial processes, and discuss two ways of computing item-to-item distances in the museum setting. Our model was evaluated with a real-world dataset collected by tracking visitors in a museum. Overall, our model attains a higher predictive accuracy than nearestneighbour collaborative filters. In addition, the model variant using physical distances outperforms that using distances computed from item-to-item similarities.
Spatial processes (random fields) are a subclass of stochastic
processes which are applied to domains that have a
geospatial interpretation, e. g., [Diggle et al., 1998; Banerjee et al.,
2004]. They are typically used in the field of spatial statistics
to model spatial associations between a set of observations
made at certain locations, and to predict values at locations
where no observations have been made. This paper applies
such models to the prediction of a user’s interests or item
ratings in recommender systems (RS). We develop our Spatial
Process Model (SPM) by adapting a Gaussian spatial process
model to the RS scenario, and demonstrate our model’s
applicability to the task of predicting implicit ratings in the
museum domain. The use of spatial processes requires a measure
of distance between items in addition to users’ ratings. This
measure, which is non-specific (e. g., it may be a physical or
a conceptual distance), can be readily obtained in most cases.
For example, distances could be computed from feature
vectors representing the items (similarly to content-based RS),
from item-to-item similarities
Our application scenario is motivated by the need to automatically recommend exhibits to museum visitors, based on non-intrusive observations of their actions in the physical space. Employing RS in this scenario is challenging due to (1) the physical nature of the domain, (2) having exhibit viewing times rather than explicit ratings, and (3) predictions differing from recommendations (we do not want to recommend exhibits that visitors are going to see anyway). We turn the first challenge into an advantage by exploiting the fact that physical distances between exhibits are meaningful, enabling the use of walking distance between exhibits to calculate (content) distance. This supports the direct, interpretable application of spatial processes by using a simple parametric Gaussian spatial process model (with the ensuing low variance in parameter estimates), compared to more complex non-parametric approaches, e. g., [Schwaighofer et al., 2005]. The second challenge, which stems from the variable semantics of viewing times (time t for different exhibits could mean interest or boredom), is naturally addressed by SPM’s structure. The third challenge can be addressed by (a) using SPM to build a model of a visitor’s interests in unseen exhibits, (b) inferring a predictive model of a visitor’s pathway through the remainder of the museum [Bohnert et al., 2008], and (c) combining these models to recommend exhibits of interest that may be overlooked if the predicted pathway is followed.
SPM was evaluated with a real-world dataset of time spans spent by museum visitors at exhibits (viewed as implicit ratings). We compared our model’s performance to that of (1) a baseline model which delivers a non-personalised prediction, and (2) a nearest-neighbour collaborative filter incorporating performance-enhancing modifications, e. g., [James and Stein, 1961; Herlocker et al., 1999]. Our results show that SPM significantly outperforms both models.
The paper is organised as follows. In Section 2, we discuss related research. Section 3 describes our domain and dataset. Our spatial processes approach for modelling and predicting exhibit interests is developed in Section 4. In Section 5, we present the results of our evaluation, followed by a discussion in Section 6 and our conclusions in Section 7. 2
Recommender systems (RS) are designed to direct users to personally interesting items in situations where the amount of available information exceeds the users’ processing capability [Resnick and Varian, 1997; Burke, 2002]. Typically, such systems (1) use information about a user (i. e., a user model) to predict ratings of items that the user has not yet considered, and (2) recommend suitable items based on these predictions. Collaborative modelling techniques constitute one of the main model classes applied in RS [Albrecht and Zukerman, 2007]. They base their predictions upon the assumption that users who have agreed in their behaviour in the past will agree in the future.
The greatest strength of collaborative approaches is that they are independent of any representation of the items being recommended, and work well for complex objects, for which features are not readily apparent. The two main collaborative approaches are memory-based and model-based. Previous research has mainly focused on memory-based approaches, such as nearest-neighbour models (classic collaborative filtering), e. g., [Herlocker et al., 1999]. The main drawback of memory-based algorithms is that they operate over the entire user database to make predictions. In contrast, model-based approaches use techniques such as Bayesian networks, latentfactor models and artificial neural networks, e. g., [Breese et al., 1998; Bell et al., 2007], to first learn a statistical model in an offline fashion, and then use it to make predictions and generate recommendations. This decomposition can significantly speed up the recommendation generation process.
Personalised guide systems in physical domains have often employed adaptable user models, which require visitors to explicitly state their interests in some form. For example, the GUIDE project [Cheverst et al., 2002] developed a handheld tourist guide for visitors to the city of Lancaster, UK. It employed a user model obtained from explicit user input to generate a dynamic and user-adapted city tour, where the order of the visited items could be varied. In the museum domain, the CHIP project [Aroyo et al., 2007] investigates how Semantic Web techniques can be used to provide personalised access to digital museum collections both online and in the physical museum, based on models that require an explicit initialisation.
Less attention has been paid to predicting preferences from non-intrusive observations, and to utilising adaptive user models that do not require explicit user input. In the museum domain, adaptive user models are usually updated from a user’s interactions with the system, the focus being on adapting content presentation as opposed to predicting and recommending exhibits to be viewed. For example, HyperAudio [Petrelli and Not, 2005] dynamically adapted the presented content and hyperlinks to stereotypical assumptions about a user, and to what a user has already accessed and seems interested in. The augmented audio reality system for museums ec(h)o [Hatala and Wakkary, 2005] treated user interests in a dynamic manner, and adapted its user model on the basis of a user’s interactions with the system. The collected user modelling data were used to deliver personalised information associated with exhibits via audio display. The PEACH project [Stock et al., 2007] developed a multimedia handheld guide which adapts its user model on the basis of both explicit visitor feedback and implicit observations of a visitor’s interactions with the device. This user model was then used to generate personalised multimedia presentations.
These systems, like most systems in the museum domain, rely on knowledge-based user models in some way, and hence, require an explicit, a-priori engineered representation of the domain knowledge. In contrast, our research investigates non-intrusive statistical user modelling and recommendation techniques that do not require such an explicit domain knowledge representation [Albrecht and Zukerman, 2007]. 3
The GECKO project endeavours to develop user modelling and personalisation techniques for information-rich physical spaces, relying on non-intrusive observations of users’ behaviour [Bohnert et al., 2008]. Developing such non-intrusive user modelling and personalisation techniques for museums requires datasets about visitor behaviour in the physical museum space (i. e., visitor pathways). Datasets that are suitable for the development phase can be obtained by manually tracking museum visitors. Such a data collection methodology is clearly inappropriate for model deployment, but it facilitates model development by eschewing issues related to technology selection and instrumentation accuracy.
Museums such as Melbourne Museum (Melbourne, Australia) display thousands of exhibits distributed over many separate galleries and exhibitions. Normally, visitors do not require recommendations to travel between individual, logically related exhibits in close physical proximity. Rather, they may prefer recommendations regarding physically separated areas. In order to gather data for assessing predictive models that support appropriate recommendations, we grouped Melbourne Museum’s individual exhibits into semantically coherent and spatially confined exhibit areas. This task, which was performed with the assistance of museum staff, yielded 126 exhibit areas. Figure 1 depicts the site map of Melbourne Museum showing these exhibit areas, together with one of the visitor pathways we collected.
To obtain our dataset, we manually tracked visitors to Melbourne Museum from April to June 2008, using a custommade tracking tool running on laptop computers [Bohnert and Zukerman, 2009]. In total, we recorded over 170 visitor pathways. We only tracked first-time adult visitors travelling on their own, to ensure that neither prior knowledge about the museum nor other visitors’ interests influenced a visitor’s decisions about which exhibits to view. Prior to the data collection, we briefed our trackers on the usage of our tracking software, the layout of the museum, and its digital representation on the site map. Additionally, we clarified what should be considered a viewing event. After the data collection, the visitor pathways were post-processed using a post-processing tool we developed. For instance, we removed tracking events that could not have possibly occurred, e. g., visitor transitions from one end of the museum to the other and back within a few seconds, or transitions outside the museum walls and back. We also removed incomplete visitor pathways, e. g., due to a laptop computer running out of battery, or a visitor leaving unexpectedly. The resulting dataset comprises 158 complete visitor pathways in the form of time-annotated sequences of visited exhibit areas, with a total visit length of 291:22:37 hours, and a total viewing time of 240:00:28 hours. The dataset also contains demographic information about the visitors, which was obtained by means of post-visit interviews conducted by our trackers. In total, we obtained 8327 viewing durations at the 126 exhibit areas, yielding an average of 52:7 exhibit areas per visitor (41:8% of the exhibit (a) Melbourne Museum – Ground level (b) Melbourne Museum – Upper level areas). Hence, on average 58:2% of the exhibit areas were not viewed by a visitor. This indicates that there is potential for pointing a visitor to relevant but unvisited exhibit areas. Table 1 summarises further statistics of the dataset.
Clearly, the deployment of an automated RS in a museum
requires suitable positioning technologies to non-intrusively
track visitors, and models to infer which exhibits are being
viewed. Although our dataset was obtained manually, it
provides information of the type that may be inferred from
sensing data
In this section, we first describe how we use viewing time to quantify interest in exhibits (Section 4.1), and discuss the applicability of spatial process models [Banerjee et al., 2004] to the prediction of a visitor’s interest in exhibits in our RS scenario (Section 4.2). We then propose a model-based collaborative approach based on the theory of Gaussian spatial processes for predicting a visitor’s (log) viewing times (viewed as exhibit interests) from non-intrusive observations of his/her (log) viewing times at visited exhibits (Section 4.3). 4.1
In an information-seeking context, people usually spend more time on relevant information than on irrelevant information, as viewing time correlates positively with preference and interest [Parsons et al., 2004]. Hence, viewing time can be used as an indirect measure of interest. We propose to use log viewing time (instead of raw viewing time), due to the following reasons. When examining our dataset (Section 3), we found the distributions of viewing times at exhibits to be positively skewed (we use the terms ‘exhibit’ and ‘exhibit area’ synonymously in the remainder of this paper). Thus, the usual assumption of a Gaussian model did not seem appropriate. To select a more appropriate family of probability distributions, we used the Bayesian Information Criterion (BIC) [Schwarz, 1978]. We tested exponential, gamma, normal, log-normal and Weibull distributions. The log-normal family fitted best, with respect to both number of best fits and average BIC score (averaged over all exhibits). Hence, we transformed all viewing times to their log-equivalent to obtain approximately normally distributed data. This transformation fits well with the idea that for high viewing times, an increase in viewing time indicates a smaller increase in the modelled interest than a similar increase in the context of low viewing times. 4.2
Spatial statistics is concerned with the analysis and
prediction of geographic data [Banerjee et al., 2004]. Utilising
spatial processes, the field deals with tasks such as modelling the
associations between observations made at certain locations,
and predicting values at locations where no observations have
been made. The assumption made for spatial processes, that
correlation between observations increases with decreasing
site distance, fits well with our RS scenario, where viewing
times are usually more correlated the more related exhibits
are. Hence, by introducing a notion of spatial distance
between exhibits to functionally specify this correlation
structure, we can use spatial process models for predicting
viewing times (i. e., exhibit interests). We use s1; : : : ; sn to
denote the locations of exhibits i; j 2 I = f1; : : : ; ng in a
space providing such a distance measure, i. e., ksi sj k. For
example, ksi sj k can be computed from feature vectors
representing the items (similarly to content-based RS), from
item-to-item similarities
Item-to-Item Distance (I2I). Item-to-item collaborative filtering [Sarwar et al., 2001] utilises a database of ratings to compute item-to-item similarities, and predicts a current user’s rating of an unseen item from his/her ratings of those items that are most similar to the item in question. Inspired by how item-to-item similarities are computed in this process, we use the observed log viewing times to derive the I2I distance measure as follows. We first transform the log viewing times into zscores by normalising the values for each visitor separately. This ensures that varying viewing behaviour does not affect the similarity computation.1 Secondly, we calculate item-to-item similarities using Pearson’s correlation coefficient on the normalised log viewing times of exhibits i and j (using only the normalised log viewing times of those visitors that have viewed both exhibits i and j). The resulting similarity value from within the interval [ 1; 1] is finally transformed into a distance measure by mapping it onto a value in [0; 1] (a similarity value of 1 yields a distance of 1, and a similarity of 1 yields a distance of 0).
Physical Distance (PD). Museums are carefully themed by curatorial staff, such that closely-related exhibits are in physical proximity. Based on this observation, we hypothesise that physical walking distance between exhibits is inversely proportional to their (content) similarity. Thus, we use physical walking distance PD as a measure of distance between exhibits. Specifically, a SVG file-based representation of Melbourne Museum was used to calculate the walking distances by mapping the site map (Figure 1) onto a graph structure which preserves the physical layout of the museum (i. e., preventing paths from passing through walls or ceilings). We normalised the resulting distances to the interval [0; 1]. 4.3
In this section, we utilise theory from the area of spatial statistics (Section 4.2) to formulate a Gaussian spatial process model, called Spatial Process Model (SPM), for predicting a museum visitor’s interests in unseen exhibits (i. e., log viewing times) from his/her viewing behaviour at visited exhibits.
Let U = f1; : : : ; mg be the set of all visitors, and I = f1; : : : ; ng be the set of all items. Typically, for a visitor u 2 U , we have viewing times for only a subset of I , say for nu exhibits. Denoting a visitor’s log viewing time vector with ru, we collect all observed log viewing times into a vector r = (r1; : : : ; rm) of dimension Pm u=1 nu. Associated with each exhibit i 2 I is a log viewing time mean i and a standard deviation i. Let = ( 1; : : : ; n) be the vector of mean log viewing times, and = ( 1; : : : ; n) the vector of standard deviations. Furthermore, u and u are the vectors of means and standard deviations respectively for only those exhibits viewed by a visitor u. For example, if visitor 1 1We also tested a variant of the I2I measure without visitor-wise normalisation. However, this variant yielded inferior results. viewed exhibits 2, 3, 7 and 9, then 1 = ( 2; 3; 7; 9) and 1 = ( 2; 3; 7; 9).
Similarly to spatial processes, SPM assumes a special
correlation structure between the viewing times of different
exhibits. In our experiments, we use a powered
exponential [Banerjee et al., 2004]:
(ksi
sj k; ; ) = exp (
( ksi
sj k) ) ,
where > 0 and 0 < < 2. That is, (ksi sj k; ; )
models the correlation between the log viewing times of
exhibits i and j ( (ksi sj k; ; ) depends on the sites si
and sj of exhibits i and j only through the distance
ksi sj k). Let H ( ; ) be a correlation matrix with
components (H ( ; ))ij = (ksi sj k; ; ) collecting all these
correlations, and let Hu( ; ) denote a visitor u’s correlation
matrix (dimension nu nu). That is, Hu( ; ) corresponds
to H ( ; ) without the rows and columns for unvisited
exhibits. Also, let = ; ; 2; ; be a vector representing
the 2n + 3 model parameters, where 2 denotes the variance
of non-spatial error terms necessary to fully specify the model
(these terms model non-spatial variation in the data). Then,
modelling the data using Gaussian spatial processes
N ( u; u) for all u = 1; : : : ; m, (1) where u = u1nu Hu( ; ) u1nu + 21nu is a visitor u’s covariance matrix, and 1nu is the identity matrix of dimension nu nu.
We employ Bayesian inference using SPM’s likelihood
function derived from Equation 1 to estimate from r
Given the model parameters = ; ; 2; ; , our model is fully specified, and we can use standard multivariate normal theory to predict a current visitor a’s log viewing times of unseen exhibits, say ra;1, from a vector of observed log viewing times ra;2. This is because (ra;1; ra;2) j is normally distributed (similarly to Equation 1). If we use the following notation ra;1 ra;2 j
N a;1 a;2 ; a;11 T a;12 a;12 a;22 then the conditional distribution p (ra;1jra;2; ) is normal with mean vector and covariance matrix
E (ra;1jra;2; ) Cov (ra;1jra;2; ) = = a;1 + a;11 a;12 a;122 (ra;2
1 T a;12 a;22 a;12, a;2) , where E (ra;1jra;2; ) represents a personalised prediction of the log viewing times ra;1. Additionally, a measure of confidence in this prediction can be easily derived from Cov (ra;1jra;2; ), i. e., by using the variances on the diagonal of this matrix.
Being a model-based approach, SPM offers advantages over memory-based collaborative filters. For instance, the model parameters = ; ; 2; ; have a clear interpretation, and the confidence measure provided by the model supports an informed interpretation of the model’s predictions. Additionally, recommendation generation is sped up by decoupling the model-fitting phase from the prediction phase. 5
This section reports on the results of an evaluation performed with our dataset (Section 3), including comparison with a nearest-neighbour collaborative filter.2 5.1
To evaluate the predictive performance of our Spatial Process Model (SPM), we implemented two additional models: Mean Model (MM) and Collaborative Filter (CF). MM, which we use as a baseline, predicts the log viewing time of an exhibit area i to be its (non-personalised) mean log viewing time i. For CF, we implemented a nearest-neighbour collaborative filtering algorithm, and added modifications from the literature that improve its performance, such as shrinkage to the mean [James and Stein, 1961] and significance weighting [Herlocker et al., 1999]. Additionally, to ensure that varying exhibit area complexity does not affect the similarity computation for selecting the nearest neighbours (viewing time increases with exhibit complexity), we transformed the log viewing times into z-scores by normalising the values for each of the exhibit areas separately. Visitor-to-visitor differences with respect to their mean viewing durations were removed by transforming predictions to the current visitor’s viewing-time scale [Herlocker et al., 1999]. Refer to [Bohnert and Zukerman, 2009] for a detailed description of CF. We tested several thousand different parameterisations, but in this paper, we report only on the performance of the best one.
Due to the relatively small dataset, we used leave-one-out cross validation to evaluate the performance of the different models. That is, for each visitor, we trained the models with a reduced dataset containing the data of 157 of the 158 visit trajectories, and used the withheld visitor pathway for testing. To train and instantiate the SPM variants (i. e., SPM-I2I and SPM-PD), we obtained a sample of = ; ; 2; ; from p( jr) by performing slice Gibbs sampling [Neal, 2003] on the training data. For each of the 129 free model parameters,3 we used (uninformative) independent uniform prior distributions. We used every 20-th sample after a burn-in phase of 1000 iterations as a sample of from p( jr), and stopped the sampling procedure after 8000 iterations. Thus, in total, we obtained 350 samples of from p( jr). This procedure 2For our experiments, we ignore travel between exhibit areas, and collapse multiple viewing events of one area into one event.
3We set i = q r2;i 2 to speed up the sampling process, where 2
r;i denotes the sample variance of the log viewing times at exhibit i, calculated from the observed log viewing times rui. This reduces the number of free parameters from 255 (126 2+3) to 129. was followed to obtain samples of for both SPM variants, i. e., for both distance measures I2I and PD (Section 4.2). We then used the posterior means estimated from these samples to compute predictions by conditioning a multivariate normal distribution (Section 4.3). We improved SPM-I2I’s predictive performance by using the (non-personalised) mean log viewing time i as a prediction whenever the conditioning would have been based on fewer than K log viewing times (in our case, K = 19). This modification was not applied to SPM-PD. For CF, predictions were computed from the ratings of the nearest neighbours; and for MM, we used i, estimated from the appropriate reduced dataset, as a prediction.
We performed two types of experiments: Individual Exhibit and Progressive Visit.
Individual Exhibit (IE). IE evaluates predictive performance for a single exhibit. For each observed visitorexhibit area pair (u; i), we removed the observation rui from the vector of visitor u’s log viewing durations, and computed a prediction r^ui from the other observations. This experiment is lenient in the sense that all available observations except the observation for exhibit area i are kept in a visitor’s viewing duration vector.
Progressive Visit (PV). PV evaluates performance as a museum visit progresses, i. e., as the number of viewed exhibit areas increases. For each visitor, we started with an empty visit, and iteratively added each viewed exhibit area to the visit history, together with its log viewing time. We then predicted the log viewing times of all yet unvisited exhibit areas.
For both experiments, we used the mean absolute error (MAE) to measure predictive accuracy as follows: MAE = P 1
X X u2U jIuj u2U i2Iu jrui r^uij, where Iu denotes a visitor u’s set of exhibit areas for which predictions were computed. For IE, we calculated the total MAE for all valid visitor-exhibit area pairs; and for PV, we computed the MAE for the yet unvisited exhibit areas for all visitors at each time fraction of a visit (to account for different visit lengths, we normalised all visits to a length of 1). 5.2
Table 2 shows the results for the IE experiment, where both spatial models (SPM-I2I and SPM-PD) outperform both MM and CF. Specifically, SPM-I2I achieves an MAE of 0:7756 (stderr 0:0068), and SPM-PD attains an MAE of 0:7548 (stderr 0:0066), outperforming SPM-I2I as well. The pairwise performance differences are statistically significant with p 0:01 for all model pairings.
The performance of SPM-PD, SPM-I2I, CF and the baseline MM for the PV experiment is depicted in Figure 2. CF outperforms MM slightly (statistically significantly for visit fractions 0:191 to 0:374 and for several shorter intervals later on, p < 0:05). More importantly, both SPM-I2I and SPM-PD perform significantly better than MM and CF. For SPM-I2I, this performance increase is statistically significant for visit fractions 0:189 to 0:960 when comparing to MM, and except
MAE for a few short intervals, for visit fractions 0:375 to 0:902 when comparing to CF. In comparison, SPM-PD performs significantly better than both MM and CF for visit fractions 0:019 to 0:922 (statistically significantly, p < 0:05). Additionally, SPM-PD outperforms SPM-I2I until visit fraction 0:660 (statistically significantly, p < 0:05). Drawing attention to the initial portion of the visits, SPM-PD’s MAE decreases rapidly, whereas the MAE for MM and CF remains at a higher level. Generally, the faster a model adapts to a visitor’s interests, the more likely it is to quickly deliver (personally) useful recommendations. Such behaviour in the early stages of a museum visit is essential in order to build trust in the RS, and to guide a visitor in a phase of the visit where such guidance is most likely needed. A similar improvement in performance cannot be observed for SPM-I2I, which suggests that a visitor’s exhibit interests observed in close physical proximity are better predictors of interests in unseen exhibits than interests in exhibits with positively correlated viewing times. As expected, MM performs at a relatively constant MAE level. For CF, SPM-I2I and SPM-PD we expected to see an improvement in performance (relative to MM) as the number of visited exhibit areas increases. However, this trend is rather subtle (it can be observed when plotting the models’ performance relative to MM). Additionally, for all four models, there is a performance drop towards the end of a visit. We postulate that these phenomena may be explained, at least partially, by the increased influence of outliers on the MAE as the number of exhibit areas remaining to be viewed is reduced with the progression of a visit. This influence in turn offsets potential gains in performance obtained from additional observations. Our hypothesis is supported by a widening in the standard error bands for all models as a visit progresses, in particular towards the end (not shown in Figure 2 for clarity of presentation). However, this behaviour requires further, more rigorous investigation.
SPM offers advantages over other model-based approaches in that, unlike neural networks (and memory-based techniques), it returns the confidence in a prediction, and its parameters have a clear interpretation; unlike Bayesian networks, our model does not require a domain-specific adaptation, such as designing the network topology. In addition, the distance measure endows our model with capabilities of hybrid RS [Burke, 2002; Albrecht and Zukerman, 2007] by seamlessly supporting the incorporation of other types of models (e. g., content-based). The distance measure also alleviates the cold-start problem. The new-item problem is addressed by utilising the (distance-based) correlation between this item and the other items. The new-user problem is similarly handled through the correlation between items rated by a user and the other items (when utilising Physical Distance as the distance measure, our model can make useful personalised predictions after only one item has been rated).
Our dataset is relatively small compared to other real-world RS applications. Although a high number of ratings per user slows down the slice Gibbs sampler due to repeated inversion of matrices of high dimension, employing our model with larger datasets should not represent a problem in practice. This is because the number of ratings per user is usually small compared to the number of users and items, and the computational complexity of evaluating the likelihood function depends only linearly on the number of users in the database. 7
In this paper, we utilised the theory of spatial processes to develop a model-based approach for predicting users’ interests in exhibits (i. e., implicit item ratings) within a RS in the museum domain. We applied our model to a real-world dataset collected by tracking visitors in a museum, using two measures of item-to-item (content) distance: (1) distances computed from item-to-item similarities (as in item-to-item collaborative filtering), and (2) physical walking distance. For both distance measures, our model attains a higher predictive accuracy than nearest-neighbour collaborative filters. Additionally, the model variant using physical distances outperforms that using distances computed from item-to-item similarities. Under the realistic Progressive Visit setting, our model using physical distance to measure item-to-item distance rapidly adapts to a user’s ratings (starting from as little as one rating), thus alleviating the new-user problem common to collaborative filtering. This is not the case for the model variant based on distance computed from item-to-item similarities, which suggests that a visitor’s interests observed for exhibits in close physical proximity are better predictors of interests in unseen exhibits than those interests for exhibits with positively correlated viewing times.
In the future, we intend to hybridise our model by incorporating content-based item features into our distance measure, and to explore hybrids of models utilising a variety of item-to-item distances. We also plan to extend our model to fit non-Gaussian item ratings, e. g., [Diggle et al., 1998; Yu et al., 2006].
This research was supported in part by grant DP0770931 from the Australian Research Council. The authors thank Carolyn Meehan and her team from Museum Victoria for their assistance; and David Abramson, Jeff Tan and Blair Bethwaite for their help with the computer cluster.